A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
Sequential operating programs and data which instruct the functioning of a digital computer.
In INFORMATION RETRIEVAL, machine-sensing or identification of visible patterns (shapes, forms, and configurations). (Harrod's Librarians' Glossary, 7th ed)
Computer-based representation of physical systems and phenomena such as chemical processes.
A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets.
The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.
Theory and development of COMPUTER SYSTEMS which perform tasks that normally require human intelligence. Such tasks may include speech recognition, LEARNING; VISUAL PERCEPTION; MATHEMATICAL COMPUTING; reasoning, PROBLEM SOLVING, DECISION-MAKING, and translation of language.
Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.
Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)
A set of statistical methods used to group variables or observations into strongly inter-related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health-related phenomenon with well-defined distribution patterns in relation to time or place or both.
A technique of inputting two-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer.
A process that includes the determination of AMINO ACID SEQUENCE of a protein (or peptide, oligopeptide or peptide fragment) and the information analysis of the sequence.
The arrangement of two or more amino acid or base sequences from an organism or organisms in such a way as to align areas of the sequences sharing common properties. The degree of relatedness or homology between the sequences is predicted computationally or statistically based on weights assigned to the elements aligned between the sequences. This in turn can serve as a potential indicator of the genetic relatedness between the organisms.
Methods developed to aid in the interpretation of ultrasound, radiographic images, etc., for diagnosis of disease.
Devices or objects in various imaging techniques used to visualize or enhance visualization by simulating conditions encountered in the procedure. Phantoms are used very often in procedures employing or measuring x-irradiation or radioactive material to evaluate performance. Phantoms often have properties similar to human tissue. Water demonstrates absorbing properties similar to normal tissue, hence water-filled phantoms are used to map radiation levels. Phantoms are used also as teaching aids to simulate real conditions with x-ray or ultrasonic machines. (From Iturralde, Dictionary and Handbook of Nuclear Medicine and Clinical Imaging, 1990)
Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
Computer-assisted processing of electric, ultrasonic, or electronic signals to interpret function and activity.
The act of testing the software for compliance with a standard.
The process of generating three-dimensional images by electronic, photographic, or other methods. For example, three-dimensional images can be generated by assembling multiple tomographic images with the aid of a computer, while photographic 3-D images (HOLOGRAPHY) can be made by exposing film to the interference pattern created when two laser light sources shine on an object.
A multistage process that includes cloning, physical mapping, subcloning, determination of the DNA SEQUENCE, and information analysis.
Improvement of the quality of a picture by various techniques, including computer processing, digital filtering, echocardiographic techniques, light and ultrastructural MICROSCOPY, fluorescence spectrometry and microscopy, scintigraphy, and in vitro image processing at the molecular level.
A stochastic process such that the conditional probability distribution for a state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system.
Linear POLYPEPTIDES that are synthesized on RIBOSOMES and may be further modified, crosslinked, cleaved, or assembled into complex proteins with several subunits. The specific sequence of AMINO ACIDS determines the shape the polypeptide will take, during PROTEIN FOLDING, and the function of the protein.
Databases containing information about PROTEINS such as AMINO ACID SEQUENCE; PROTEIN CONFORMATION; and other properties.
A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.
The determination of the pattern of genes expressed at the level of GENETIC TRANSCRIPTION, under specific circumstances or in a specific cell.
In statistics, a technique for numerically approximating the solution of a mathematical problem by studying the distribution of some random variable, often generated by a computer. The name alludes to the randomness characteristic of the games of chance played at the gambling casinos in Monte Carlo. (From Random House Unabridged Dictionary, 2d ed, 1993)
The process of pictorial communication, between human and computers, in which the computer input and output have the form of charts, drawings, or other appropriate pictorial representation.
Controlled operation of an apparatus, process, or system by mechanical or electronic devices that take the place of human organs of observation, effort, and decision. (From Webster's Collegiate Dictionary, 1993)
Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references.
Hybridization of a nucleic acid sample to a very large set of OLIGONUCLEOTIDE PROBES, which have been attached individually in columns and rows to a solid support, to determine a BASE SEQUENCE, or to detect variations in a gene sequence, GENE EXPRESSION, or for GENE MAPPING.
A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.
Computer-assisted study of methods for obtaining useful quantitative solutions to problems that have been expressed mathematically.
Theoretical representations that simulate the behavior or activity of systems, processes, or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
The portion of an interactive computer program that issues messages to and receives commands from a user.
Information application based on a variety of coding methods to minimize the amount of data to be stored, retrieved, or transmitted. Data compression can be applied to various forms of data, such as images and signals. It is used to reduce costs and increase efficiency in the maintenance of large volumes of data.
Approximate, quantitative reasoning that is concerned with the linguistic ambiguity which exists in natural or synthetic language. At its core are variables such as good, bad, and young as well as modifiers such as more, less, and very. These ordinary terms represent fuzzy sets in a particular problem. Fuzzy logic plays a key role in many medical expert systems.
Any visible result of a procedure which is caused by the procedure itself and not by the entity being analyzed. Common examples include histological structures introduced by tissue processing, radiographic images of structures that are not naturally present in living tissue, and products of chemical reactions that occur during analysis.
Application of computer programs designed to assist the physician in solving a diagnostic problem.
Databases devoted to knowledge about specific genes and gene products.
Application of statistical procedures to analyze specific observed or assumed facts from a particular study.
Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.
Continuous frequency distribution of infinite range. Its properties are as follows: 1, continuous, symmetrical distribution with both tails extending to infinity; 2, arithmetic mean, mode, and median identical; and 3, shape completely determined by the mean and standard deviation.
Organized activities related to the storage, location, search, and retrieval of information.
Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.
Computer systems or networks designed to provide radiographic interpretive information.
The systematic study of the complete DNA sequences (GENOME) of organisms.
A loose confederation of computer communication networks around the world. The networks that make up the Internet are connected through several backbone networks. The Internet grew out of the US Government ARPAnet project and was designed to facilitate information exchange.
A graphic device used in decision analysis, series of decision options are represented as branches (hierarchical).
Improvement in the quality of an x-ray image by use of an intensifying screen, tube, or filter and by optimum exposure techniques. Digital processing methods are often employed.
Combination or superimposition of two images for demonstrating differences between them (e.g., radiograph with contrast vs. one without, radionuclide images using different radionuclides, radiograph vs. radionuclide image) and in the preparation of audiovisual materials (e.g., offsetting identical images, coloring of vessels in angiograms).
Specific languages used to prepare computer programs.
Signal and data processing method that uses decomposition of wavelets to approximate, estimate, or compress signals with finite time and frequency domains. It represents a signal or data in terms of a fast decaying wavelet series from the original prototype wavelet, called the mother wavelet. This mathematical algorithm has been adopted widely in biomedical disciplines for data and signal processing in noise removal and audio/image compression (e.g., EEG and MRI).
Computer-assisted analysis and processing of problems in a particular area.
The comparison of the quantity of meaningful data to the irrelevant or incorrect data.
Use of sophisticated analysis tools to sort through, organize, examine, and combine large sets of information.
Methods for determining interaction between PROTEINS.
Models used experimentally or theoretically to study molecular shape, electronic properties, or interactions; includes analogous molecules, computer-generated graphics, and mechanical structures.
Techniques using energy such as radio frequency, infrared light, laser light, visible light, or acoustic energy to transfer information without the use of wires, over both short and long distances.
Learning algorithms which are a set of related supervised computer learning methods that analyze data and recognize patterns, and used for classification and regression analysis.
Data processing largely performed by automatic means.
Specifications and instructions applied to the software.
A multistage process that includes cloning, physical mapping, subcloning, sequencing, and information analysis of an RNA SEQUENCE.
A computer in a medical context is an electronic device that processes, stores, and retrieves data, often used in medical settings for tasks such as maintaining patient records, managing diagnostic images, and supporting clinical decision-making through software applications and tools.
Descriptions of specific amino acid, carbohydrate, or nucleotide sequences which have appeared in the published literature and/or are deposited in and maintained by databanks such as GENBANK, European Molecular Biology Laboratory (EMBL), National Biomedical Research Foundation (NBRF), or other sequence repositories.
Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables.
The genetic complement of an organism, including all of its GENES, as represented in its DNA, or in some cases, its RNA.
Interacting DNA-encoded regulatory subsystems in the GENOME that coordinate input from activator and repressor TRANSCRIPTION FACTORS during development, cell differentiation, or in response to environmental cues. The networks function to ultimately specify expression of particular sets of GENES for specific conditions, times, or locations.
A graphic means for assessing the ability of a screening test to discriminate between healthy and diseased persons; may also be used in other studies, e.g., distinguishing stimuli responses as to a faint stimuli or nonstimuli.
Methods of creating machines and devices.
Theoretical representations that simulate the behavior or activity of chemical processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment.
The study of chance processes or the relative frequency characterizing a chance process.
In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.
Any method used for determining the location of and relative distances between genes on a chromosome.
The relationships of groups of organisms as reflected by their genetic makeup.
Non-invasive method of demonstrating internal anatomy based on the principle that atomic nuclei in a strong magnetic field absorb pulses of radiofrequency energy and emit them as radiowaves which can be reconstructed into computerized images. The concept includes proton spin tomographic techniques.
Elements of limited time intervals, contributing to particular results or situations.
The sequence of PURINES and PYRIMIDINES in nucleic acids and polynucleotides. It is also called nucleotide sequence.
A statistical analytic technique used with discrete dependent variables, concerned with separating sets of observed values and allocating new values. It is sometimes used instead of regression analysis.
Computed tomography modalities which use a cone or pyramid-shaped beam of radiation.
Tomography using x-ray transmission and a computer algorithm to reconstruct the image.
A principle of estimation in which the estimates of a set of parameters in a statistical model are those quantities minimizing the sum of squared differences between the observed values of a dependent variable and the values predicted by the model.
The study of systems which respond disproportionately (nonlinearly) to initial conditions or perturbing stimuli. Nonlinear systems may exhibit "chaos" which is classically characterized as sensitive dependence on initial conditions. Chaotic systems, while distinguished from more ordered periodic systems, are not random. When their behavior over time is appropriately displayed (in "phase space"), constraints are evident which are described by "strange attractors". Phase space representations of chaotic systems, or strange attractors, usually reveal fractal (FRACTALS) self-similarity across time scales. Natural, including biological, systems often display nonlinear dynamics and chaos.
A technique of operations research for solving certain kinds of problems involving many variables where a best value or set of best values is to be found. It is most likely to be feasible when the quantity to be optimized, sometimes called the objective function, can be stated as a mathematical expression in terms of the various activities within the system, and when this expression is simply proportional to the measure of the activities, i.e., is linear, and when all the restrictions are also linear. It is different from computer programming, although problems using linear programming techniques may be programmed on a computer.
The evaluation of incidents involving the loss of function of a device. These evaluations are used for a variety of purposes such as to determine the failure rates, the causes of failures, costs of failures, and the reliability and maintainability of devices.
The complete genetic complement contained in the DNA of a set of CHROMOSOMES in a HUMAN. The length of the human genome is about 3 billion base pairs.
The systematic study of the complete complement of proteins (PROTEOME) of organisms.
Databases containing information about NUCLEIC ACIDS such as BASE SEQUENCE; SNPS; NUCLEIC ACID CONFORMATION; and other properties. Information about the DNA fragments kept in a GENE LIBRARY or GENOMIC LIBRARY is often maintained in DNA databases.
Mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components.
A single nucleotide variation in a genetic sequence that occurs at appreciable frequency in the population.
The order of amino acids as they occur in a polypeptide chain. This is referred to as the primary structure of proteins. It is of fundamental importance in determining PROTEIN CONFORMATION.
A system containing any combination of computers, computer terminals, printers, audio or visual display devices, or telephones interconnected by telecommunications equipment or cables: used to transmit or receive information. (Random House Unabridged Dictionary, 2d ed)
Computer processing of a language with rules that reflect and describe current usage rather than prescribed usage.
Imaging methods that result in sharp images of objects located on a chosen plane and blurred images located above or below the plane.
The protein complement of an organism coded for by its genome.

An effective approach for analyzing "prefinished" genomic sequence data. (1/42270)

Ongoing efforts to sequence the human genome are already generating large amounts of data, with substantial increases anticipated over the next few years. In most cases, a shotgun sequencing strategy is being used, which rapidly yields most of the primary sequence in incompletely assembled sequence contigs ("prefinished" sequence) and more slowly produces the final, completely assembled sequence ("finished" sequence). Thus, in general, prefinished sequence is produced in excess of finished sequence, and this trend is certain to continue and even accelerate over the next few years. Even at a prefinished stage, genomic sequence represents a rich source of important biological information that is of great interest to many investigators. However, analyzing such data is a challenging and daunting task, both because of its sheer volume and because it can change on a day-by-day basis. To facilitate the discovery and characterization of genes and other important elements within prefinished sequence, we have developed an analytical strategy and system that uses readily available software tools in new combinations. Implementation of this strategy for the analysis of prefinished sequence data from human chromosome 7 has demonstrated that this is a convenient, inexpensive, and extensible solution to the problem of analyzing the large amounts of preliminary data being produced by large-scale sequencing efforts. Our approach is accessible to any investigator who wishes to assimilate additional information about particular sequence data en route to developing richer annotations of a finished sequence.  (+info)

A computational screen for methylation guide snoRNAs in yeast. (2/42270)

Small nucleolar RNAs (snoRNAs) are required for ribose 2'-O-methylation of eukaryotic ribosomal RNA. Many of the genes for this snoRNA family have remained unidentified in Saccharomyces cerevisiae, despite the availability of a complete genome sequence. Probabilistic modeling methods akin to those used in speech recognition and computational linguistics were used to computationally screen the yeast genome and identify 22 methylation guide snoRNAs, snR50 to snR71. Gene disruptions and other experimental characterization confirmed their methylation guide function. In total, 51 of the 55 ribose methylated sites in yeast ribosomal RNA were assigned to 41 different guide snoRNAs.  (+info)

Referenceless interleaved echo-planar imaging. (3/42270)

Interleaved echo-planar imaging (EPI) is an ultrafast imaging technique important for applications that require high time resolution or short total acquisition times. Unfortunately, EPI is prone to significant ghosting artifacts, resulting primarily from system time delays that cause data matrix misregistration. In this work, it is shown mathematically and experimentally that system time delays are orientation dependent, resulting from anisotropic physical gradient delays. This analysis characterizes the behavior of time delays in oblique coordinates, and a new ghosting artifact caused by anisotropic delays is described. "Compensation blips" are proposed for time delay correction. These blips are shown to remove the effects of anisotropic gradient delays, eliminating the need for repeated reference scans and postprocessing corrections. Examples of phantom and in vivo images are shown.  (+info)

An evaluation of elongation factor 1 alpha as a phylogenetic marker for eukaryotes. (4/42270)

Elongation factor 1 alpha (EF-1 alpha) is a highly conserved ubiquitous protein involved in translation that has been suggested to have desirable properties for phylogenetic inference. To examine the utility of EF-1 alpha as a phylogenetic marker for eukaryotes, we studied three properties of EF-1 alpha trees: congruency with other phyogenetic markers, the impact of species sampling, and the degree of substitutional saturation occurring between taxa. Our analyses indicate that the EF-1 alpha tree is congruent with some other molecular phylogenies in identifying both the deepest branches and some recent relationships in the eukaryotic line of descent. However, the topology of the intermediate portion of the EF-1 alpha tree, occupied by most of the protist lineages, differs for different phylogenetic methods, and bootstrap values for branches are low. Most problematic in this region is the failure of all phylogenetic methods to resolve the monophyly of two higher-order protistan taxa, the Ciliophora and the Alveolata. JACKMONO analyses indicated that the impact of species sampling on bootstrap support for most internal nodes of the eukaryotic EF-1 alpha tree is extreme. Furthermore, a comparison of observed versus inferred numbers of substitutions indicates that multiple overlapping substitutions have occurred, especially on the branch separating the Eukaryota from the Archaebacteria, suggesting that the rooting of the eukaryotic tree on the diplomonad lineage should be treated with caution. Overall, these results suggest that the phylogenies obtained from EF-1 alpha are congruent with other molecular phylogenies in recovering the monophyly of groups such as the Metazoa, Fungi, Magnoliophyta, and Euglenozoa. However, the interrelationships between these and other protist lineages are not well resolved. This lack of resolution may result from the combined effects of poor taxonomic sampling, relatively few informative positions, large numbers of overlapping substitutions that obscure phylogenetic signal, and lineage-specific rate increases in the EF-1 alpha data set. It is also consistent with the nearly simultaneous diversification of major eukaryotic lineages implied by the "big-bang" hypothesis of eukaryote evolution.  (+info)

Hierarchical cluster analysis applied to workers' exposures in fiberglass insulation manufacturing. (5/42270)

The objectives of this study were to explore the application of cluster analysis to the characterization of multiple exposures in industrial hygiene practice and to compare exposure groupings based on the result from cluster analysis with that based on non-measurement-based approaches commonly used in epidemiology. Cluster analysis was performed for 37 workers simultaneously exposed to three agents (endotoxin, phenolic compounds and formaldehyde) in fiberglass insulation manufacturing. Different clustering algorithms, including complete-linkage (or farthest-neighbor), single-linkage (or nearest-neighbor), group-average and model-based clustering approaches, were used to construct the tree structures from which clusters can be formed. Differences were observed between the exposure clusters constructed by these different clustering algorithms. When contrasting the exposure classification based on tree structures with that based on non-measurement-based information, the results indicate that the exposure clusters identified from the tree structures had little in common with the classification results from either the traditional exposure zone or the work group classification approach. In terms of the defining homogeneous exposure groups or from the standpoint of health risk, some toxicological normalization in the components of the exposure vector appears to be required in order to form meaningful exposure groupings from cluster analysis. Finally, it remains important to see if the lack of correspondence between exposure groups based on epidemiological classification and measurement data is a peculiarity of the data or a more general problem in multivariate exposure analysis.  (+info)

A new filtering algorithm for medical magnetic resonance and computer tomography images. (6/42270)

Inner views of tubular structures based on computer tomography (CT) and magnetic resonance (MR) data sets may be created by virtual endoscopy. After a preliminary segmentation procedure for selecting the organ to be represented, the virtual endoscopy is a new postprocessing technique using surface or volume rendering of the data sets. In the case of surface rendering, the segmentation is based on a grey level thresholding technique. To avoid artifacts owing to the noise created in the imaging process, and to restore spurious resolution degradations, a robust Wiener filter was applied. This filter working in Fourier space approximates the noise spectrum by a simple function that is proportional to the square root of the signal amplitude. Thus, only points with tiny amplitudes consisting mostly of noise are suppressed. Further artifacts are avoided by the correct selection of the threshold range. Afterwards, the lumen and the inner walls of the tubular structures are well represented and allow one to distinguish between harmless fluctuations and medically significant structures.  (+info)

Efficacy of ampicillin plus ceftriaxone in treatment of experimental endocarditis due to Enterococcus faecalis strains highly resistant to aminoglycosides. (7/42270)

The purpose of this work was to evaluate the in vitro possibilities of ampicillin-ceftriaxone combinations for 10 Enterococcus faecalis strains with high-level resistance to aminoglycosides (HLRAg) and to assess the efficacy of ampicillin plus ceftriaxone, both administered with humanlike pharmacokinetics, for the treatment of experimental endocarditis due to HLRAg E. faecalis. A reduction of 1 to 4 dilutions in MICs of ampicillin was obtained when ampicillin was combined with a fixed subinhibitory ceftriaxone concentration of 4 micrograms/ml. This potentiating effect was also observed by the double disk method with all 10 strains. Time-kill studies performed with 1 and 2 micrograms of ampicillin alone per ml or in combination with 5, 10, 20, 40, and 60 micrograms of ceftriaxone per ml showed a > or = 2 log10 reduction in CFU per milliliter with respect to ampicillin alone and to the initial inoculum for all 10 E. faecalis strains studied. This effect was obtained for seven strains with the combination of 2 micrograms of ampicillin per ml plus 10 micrograms of ceftriaxone per ml and for six strains with 5 micrograms of ceftriaxone per ml. Animals with catheter-induced endocarditis were infected intravenously with 10(8) CFU of E. faecalis V48 or 10(5) CFU of E. faecalis V45 and were treated for 3 days with humanlike pharmacokinetics of 2 g of ampicillin every 4 h, alone or combined with 2 g of ceftriaxone every 12 h. The levels in serum and the pharmacokinetic parameters of the humanlike pharmacokinetics of ampicillin or ceftriaxone in rabbits were similar to those found in humans treated with 2 g of ampicillin or ceftriaxone intravenously. Results of the therapy for experimental endocarditis caused by E. faecalis V48 or V45 showed that the residual bacterial titers in aortic valve vegetations were significantly lower in the animals treated with the combinations of ampicillin plus ceftriaxone than in those treated with ampicillin alone (P < 0.001). The combination of ampicillin and ceftriaxone showed in vitro and in vivo synergism against HLRAg E. faecalis.  (+info)

The muscle chloride channel ClC-1 has a double-barreled appearance that is differentially affected in dominant and recessive myotonia. (8/42270)

Single-channel recordings of the currents mediated by the muscle Cl- channel, ClC-1, expressed in Xenopus oocytes, provide the first direct evidence that this channel has two equidistant open conductance levels like the Torpedo ClC-0 prototype. As for the case of ClC-0, the probabilities and dwell times of the closed and conducting states are consistent with the presence of two independently gated pathways with approximately 1.2 pS conductance enabled in parallel via a common gate. However, the voltage dependence of the common gate is different and the kinetics are much faster than for ClC-0. Estimates of single-channel parameters from the analysis of macroscopic current fluctuations agree with those from single-channel recordings. Fluctuation analysis was used to characterize changes in the apparent double-gate behavior of the ClC-1 mutations I290M and I556N causing, respectively, a dominant and a recessive form of myotonia. We find that both mutations reduce about equally the open probability of single protopores and that mutation I290M yields a stronger reduction of the common gate open probability than mutation I556N. Our results suggest that the mammalian ClC-homologues have the same structure and mechanism proposed for the Torpedo channel ClC-0. Differential effects on the two gates that appear to modulate the activation of ClC-1 channels may be important determinants for the different patterns of inheritance of dominant and recessive ClC-1 mutations.  (+info)

An algorithm is not a medical term, but rather a concept from computer science and mathematics. In the context of medicine, algorithms are often used to describe step-by-step procedures for diagnosing or managing medical conditions. These procedures typically involve a series of rules or decision points that help healthcare professionals make informed decisions about patient care.

For example, an algorithm for diagnosing a particular type of heart disease might involve taking a patient's medical history, performing a physical exam, ordering certain diagnostic tests, and interpreting the results in a specific way. By following this algorithm, healthcare professionals can ensure that they are using a consistent and evidence-based approach to making a diagnosis.

Algorithms can also be used to guide treatment decisions. For instance, an algorithm for managing diabetes might involve setting target blood sugar levels, recommending certain medications or lifestyle changes based on the patient's individual needs, and monitoring the patient's response to treatment over time.

Overall, algorithms are valuable tools in medicine because they help standardize clinical decision-making and ensure that patients receive high-quality care based on the latest scientific evidence.

I am not aware of a widely accepted medical definition for the term "software," as it is more commonly used in the context of computer science and technology. Software refers to programs, data, and instructions that are used by computers to perform various tasks. It does not have direct relevance to medical fields such as anatomy, physiology, or clinical practice. If you have any questions related to medicine or healthcare, I would be happy to try to help with those instead!

Automated Pattern Recognition in a medical context refers to the use of computer algorithms and artificial intelligence techniques to identify, classify, and analyze specific patterns or trends in medical data. This can include recognizing visual patterns in medical images, such as X-rays or MRIs, or identifying patterns in large datasets of physiological measurements or electronic health records.

The goal of automated pattern recognition is to assist healthcare professionals in making more accurate diagnoses, monitoring disease progression, and developing personalized treatment plans. By automating the process of pattern recognition, it can help reduce human error, increase efficiency, and improve patient outcomes.

Examples of automated pattern recognition in medicine include using machine learning algorithms to identify early signs of diabetic retinopathy in eye scans or detecting abnormal heart rhythms in electrocardiograms (ECGs). These techniques can also be used to predict patient risk based on patterns in their medical history, such as identifying patients who are at high risk for readmission to the hospital.

A computer simulation is a process that involves creating a model of a real-world system or phenomenon on a computer and then using that model to run experiments and make predictions about how the system will behave under different conditions. In the medical field, computer simulations are used for a variety of purposes, including:

1. Training and education: Computer simulations can be used to create realistic virtual environments where medical students and professionals can practice their skills and learn new procedures without risk to actual patients. For example, surgeons may use simulation software to practice complex surgical techniques before performing them on real patients.
2. Research and development: Computer simulations can help medical researchers study the behavior of biological systems at a level of detail that would be difficult or impossible to achieve through experimental methods alone. By creating detailed models of cells, tissues, organs, or even entire organisms, researchers can use simulation software to explore how these systems function and how they respond to different stimuli.
3. Drug discovery and development: Computer simulations are an essential tool in modern drug discovery and development. By modeling the behavior of drugs at a molecular level, researchers can predict how they will interact with their targets in the body and identify potential side effects or toxicities. This information can help guide the design of new drugs and reduce the need for expensive and time-consuming clinical trials.
4. Personalized medicine: Computer simulations can be used to create personalized models of individual patients based on their unique genetic, physiological, and environmental characteristics. These models can then be used to predict how a patient will respond to different treatments and identify the most effective therapy for their specific condition.

Overall, computer simulations are a powerful tool in modern medicine, enabling researchers and clinicians to study complex systems and make predictions about how they will behave under a wide range of conditions. By providing insights into the behavior of biological systems at a level of detail that would be difficult or impossible to achieve through experimental methods alone, computer simulations are helping to advance our understanding of human health and disease.

Computational biology is a branch of biology that uses mathematical and computational methods to study biological data, models, and processes. It involves the development and application of algorithms, statistical models, and computational approaches to analyze and interpret large-scale molecular and phenotypic data from genomics, transcriptomics, proteomics, metabolomics, and other high-throughput technologies. The goal is to gain insights into biological systems and processes, develop predictive models, and inform experimental design and hypothesis testing in the life sciences. Computational biology encompasses a wide range of disciplines, including bioinformatics, systems biology, computational genomics, network biology, and mathematical modeling of biological systems.

Reproducibility of results in a medical context refers to the ability to obtain consistent and comparable findings when a particular experiment or study is repeated, either by the same researcher or by different researchers, following the same experimental protocol. It is an essential principle in scientific research that helps to ensure the validity and reliability of research findings.

In medical research, reproducibility of results is crucial for establishing the effectiveness and safety of new treatments, interventions, or diagnostic tools. It involves conducting well-designed studies with adequate sample sizes, appropriate statistical analyses, and transparent reporting of methods and findings to allow other researchers to replicate the study and confirm or refute the results.

The lack of reproducibility in medical research has become a significant concern in recent years, as several high-profile studies have failed to produce consistent findings when replicated by other researchers. This has led to increased scrutiny of research practices and a call for greater transparency, rigor, and standardization in the conduct and reporting of medical research.

Artificial Intelligence (AI) in the medical context refers to the simulation of human intelligence processes by machines, particularly computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction.

In healthcare, AI is increasingly being used to analyze large amounts of data, identify patterns, make decisions, and perform tasks that would normally require human intelligence. This can include tasks such as diagnosing diseases, recommending treatments, personalizing patient care, and improving clinical workflows.

Examples of AI in medicine include machine learning algorithms that analyze medical images to detect signs of disease, natural language processing tools that extract relevant information from electronic health records, and robot-assisted surgery systems that enable more precise and minimally invasive procedures.

Statistical models are mathematical representations that describe the relationship between variables in a given dataset. They are used to analyze and interpret data in order to make predictions or test hypotheses about a population. In the context of medicine, statistical models can be used for various purposes such as:

1. Disease risk prediction: By analyzing demographic, clinical, and genetic data using statistical models, researchers can identify factors that contribute to an individual's risk of developing certain diseases. This information can then be used to develop personalized prevention strategies or early detection methods.

2. Clinical trial design and analysis: Statistical models are essential tools for designing and analyzing clinical trials. They help determine sample size, allocate participants to treatment groups, and assess the effectiveness and safety of interventions.

3. Epidemiological studies: Researchers use statistical models to investigate the distribution and determinants of health-related events in populations. This includes studying patterns of disease transmission, evaluating public health interventions, and estimating the burden of diseases.

4. Health services research: Statistical models are employed to analyze healthcare utilization, costs, and outcomes. This helps inform decisions about resource allocation, policy development, and quality improvement initiatives.

5. Biostatistics and bioinformatics: In these fields, statistical models are used to analyze large-scale molecular data (e.g., genomics, proteomics) to understand biological processes and identify potential therapeutic targets.

In summary, statistical models in medicine provide a framework for understanding complex relationships between variables and making informed decisions based on data-driven insights.

Sensitivity and specificity are statistical measures used to describe the performance of a diagnostic test or screening tool in identifying true positive and true negative results.

* Sensitivity refers to the proportion of people who have a particular condition (true positives) who are correctly identified by the test. It is also known as the "true positive rate" or "recall." A highly sensitive test will identify most or all of the people with the condition, but may also produce more false positives.
* Specificity refers to the proportion of people who do not have a particular condition (true negatives) who are correctly identified by the test. It is also known as the "true negative rate." A highly specific test will identify most or all of the people without the condition, but may also produce more false negatives.

In medical testing, both sensitivity and specificity are important considerations when evaluating a diagnostic test. High sensitivity is desirable for screening tests that aim to identify as many cases of a condition as possible, while high specificity is desirable for confirmatory tests that aim to rule out the condition in people who do not have it.

It's worth noting that sensitivity and specificity are often influenced by factors such as the prevalence of the condition in the population being tested, the threshold used to define a positive result, and the reliability and validity of the test itself. Therefore, it's important to consider these factors when interpreting the results of a diagnostic test.

Cluster analysis is a statistical method used to group similar objects or data points together based on their characteristics or features. In medical and healthcare research, cluster analysis can be used to identify patterns or relationships within complex datasets, such as patient records or genetic information. This technique can help researchers to classify patients into distinct subgroups based on their symptoms, diagnoses, or other variables, which can inform more personalized treatment plans or public health interventions.

Cluster analysis involves several steps, including:

1. Data preparation: The researcher must first collect and clean the data, ensuring that it is complete and free from errors. This may involve removing outlier values or missing data points.
2. Distance measurement: Next, the researcher must determine how to measure the distance between each pair of data points. Common methods include Euclidean distance (the straight-line distance between two points) or Manhattan distance (the distance between two points along a grid).
3. Clustering algorithm: The researcher then applies a clustering algorithm, which groups similar data points together based on their distances from one another. Common algorithms include hierarchical clustering (which creates a tree-like structure of clusters) or k-means clustering (which assigns each data point to the nearest centroid).
4. Validation: Finally, the researcher must validate the results of the cluster analysis by evaluating the stability and robustness of the clusters. This may involve re-running the analysis with different distance measures or clustering algorithms, or comparing the results to external criteria.

Cluster analysis is a powerful tool for identifying patterns and relationships within complex datasets, but it requires careful consideration of the data preparation, distance measurement, and validation steps to ensure accurate and meaningful results.

Computer-assisted image processing is a medical term that refers to the use of computer systems and specialized software to improve, analyze, and interpret medical images obtained through various imaging techniques such as X-ray, CT (computed tomography), MRI (magnetic resonance imaging), ultrasound, and others.

The process typically involves several steps, including image acquisition, enhancement, segmentation, restoration, and analysis. Image processing algorithms can be used to enhance the quality of medical images by adjusting contrast, brightness, and sharpness, as well as removing noise and artifacts that may interfere with accurate diagnosis. Segmentation techniques can be used to isolate specific regions or structures of interest within an image, allowing for more detailed analysis.

Computer-assisted image processing has numerous applications in medical imaging, including detection and characterization of lesions, tumors, and other abnormalities; assessment of organ function and morphology; and guidance of interventional procedures such as biopsies and surgeries. By automating and standardizing image analysis tasks, computer-assisted image processing can help to improve diagnostic accuracy, efficiency, and consistency, while reducing the potential for human error.

Protein sequence analysis is the systematic examination and interpretation of the amino acid sequence of a protein to understand its structure, function, evolutionary relationships, and other biological properties. It involves various computational methods and tools to analyze the primary structure of proteins, which is the linear arrangement of amino acids along the polypeptide chain.

Protein sequence analysis can provide insights into several aspects, such as:

1. Identification of functional domains, motifs, or sites within a protein that may be responsible for its specific biochemical activities.
2. Comparison of homologous sequences from different organisms to infer evolutionary relationships and determine the degree of similarity or divergence among them.
3. Prediction of secondary and tertiary structures based on patterns of amino acid composition, hydrophobicity, and charge distribution.
4. Detection of post-translational modifications that may influence protein function, localization, or stability.
5. Identification of protease cleavage sites, signal peptides, or other sequence features that play a role in protein processing and targeting.

Some common techniques used in protein sequence analysis include:

1. Multiple Sequence Alignment (MSA): A method to align multiple protein sequences to identify conserved regions, gaps, and variations.
2. BLAST (Basic Local Alignment Search Tool): A widely-used tool for comparing a query protein sequence against a database of known sequences to find similarities and infer function or evolutionary relationships.
3. Hidden Markov Models (HMMs): Statistical models used to describe the probability distribution of amino acid sequences in protein families, allowing for more sensitive detection of remote homologs.
4. Protein structure prediction: Methods that use various computational approaches to predict the three-dimensional structure of a protein based on its amino acid sequence.
5. Phylogenetic analysis: The construction and interpretation of evolutionary trees (phylogenies) based on aligned protein sequences, which can provide insights into the historical relationships among organisms or proteins.

In genetics, sequence alignment is the process of arranging two or more DNA, RNA, or protein sequences to identify regions of similarity or homology between them. This is often done using computational methods to compare the nucleotide or amino acid sequences and identify matching patterns, which can provide insight into evolutionary relationships, functional domains, or potential genetic disorders. The alignment process typically involves adjusting gaps and mismatches in the sequences to maximize the similarity between them, resulting in an aligned sequence that can be visually represented and analyzed.

Computer-assisted image interpretation is the use of computer algorithms and software to assist healthcare professionals in analyzing and interpreting medical images. These systems use various techniques such as pattern recognition, machine learning, and artificial intelligence to help identify and highlight abnormalities or patterns within imaging data, such as X-rays, CT scans, MRI, and ultrasound images. The goal is to increase the accuracy, consistency, and efficiency of image interpretation, while also reducing the potential for human error. It's important to note that these systems are intended to assist healthcare professionals in their decision making process and not to replace them.

In the field of medical imaging, "phantoms" refer to physical objects that are specially designed and used for calibration, quality control, and evaluation of imaging systems. These phantoms contain materials with known properties, such as attenuation coefficients or spatial resolution, which allow for standardized measurement and comparison of imaging parameters across different machines and settings.

Imaging phantoms can take various forms depending on the modality of imaging. For example, in computed tomography (CT), a common type of phantom is the "water-equivalent phantom," which contains materials with similar X-ray attenuation properties as water. This allows for consistent measurement of CT dose and image quality. In magnetic resonance imaging (MRI), phantoms may contain materials with specific relaxation times or magnetic susceptibilities, enabling assessment of signal-to-noise ratio, spatial resolution, and other imaging parameters.

By using these standardized objects, healthcare professionals can ensure the accuracy, consistency, and reliability of medical images, ultimately contributing to improved patient care and safety.

Genetic models are theoretical frameworks used in genetics to describe and explain the inheritance patterns and genetic architecture of traits, diseases, or phenomena. These models are based on mathematical equations and statistical methods that incorporate information about gene frequencies, modes of inheritance, and the effects of environmental factors. They can be used to predict the probability of certain genetic outcomes, to understand the genetic basis of complex traits, and to inform medical management and treatment decisions.

There are several types of genetic models, including:

1. Mendelian models: These models describe the inheritance patterns of simple genetic traits that follow Mendel's laws of segregation and independent assortment. Examples include autosomal dominant, autosomal recessive, and X-linked inheritance.
2. Complex trait models: These models describe the inheritance patterns of complex traits that are influenced by multiple genes and environmental factors. Examples include heart disease, diabetes, and cancer.
3. Population genetics models: These models describe the distribution and frequency of genetic variants within populations over time. They can be used to study evolutionary processes, such as natural selection and genetic drift.
4. Quantitative genetics models: These models describe the relationship between genetic variation and phenotypic variation in continuous traits, such as height or IQ. They can be used to estimate heritability and to identify quantitative trait loci (QTLs) that contribute to trait variation.
5. Statistical genetics models: These models use statistical methods to analyze genetic data and infer the presence of genetic associations or linkage. They can be used to identify genetic risk factors for diseases or traits.

Overall, genetic models are essential tools in genetics research and medical genetics, as they allow researchers to make predictions about genetic outcomes, test hypotheses about the genetic basis of traits and diseases, and develop strategies for prevention, diagnosis, and treatment.

Computer-assisted signal processing is a medical term that refers to the use of computer algorithms and software to analyze, interpret, and extract meaningful information from biological signals. These signals can include physiological data such as electrocardiogram (ECG) waves, electromyography (EMG) signals, electroencephalography (EEG) readings, or medical images.

The goal of computer-assisted signal processing is to automate the analysis of these complex signals and extract relevant features that can be used for diagnostic, monitoring, or therapeutic purposes. This process typically involves several steps, including:

1. Signal acquisition: Collecting raw data from sensors or medical devices.
2. Preprocessing: Cleaning and filtering the data to remove noise and artifacts.
3. Feature extraction: Identifying and quantifying relevant features in the signal, such as peaks, troughs, or patterns.
4. Analysis: Applying statistical or machine learning algorithms to interpret the extracted features and make predictions about the underlying physiological state.
5. Visualization: Presenting the results in a clear and intuitive way for clinicians to review and use.

Computer-assisted signal processing has numerous applications in healthcare, including:

* Diagnosing and monitoring cardiac arrhythmias or other heart conditions using ECG signals.
* Assessing muscle activity and function using EMG signals.
* Monitoring brain activity and diagnosing neurological disorders using EEG readings.
* Analyzing medical images to detect abnormalities, such as tumors or fractures.

Overall, computer-assisted signal processing is a powerful tool for improving the accuracy and efficiency of medical diagnosis and monitoring, enabling clinicians to make more informed decisions about patient care.

Software validation, in the context of medical devices and healthcare, is the process of evaluating software to ensure that it meets specified requirements for its intended use and that it performs as expected. This process is typically carried out through testing and other verification methods to ensure that the software functions correctly, safely, and reliably in a real-world environment. The goal of software validation is to provide evidence that the software is fit for its intended purpose and complies with relevant regulations and standards. It is an important part of the overall process of bringing a medical device or healthcare technology to market, as it helps to ensure patient safety and regulatory compliance.

Three-dimensional (3D) imaging in medicine refers to the use of technologies and techniques that generate a 3D representation of internal body structures, organs, or tissues. This is achieved by acquiring and processing data from various imaging modalities such as X-ray computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, or confocal microscopy. The resulting 3D images offer a more detailed visualization of the anatomy and pathology compared to traditional 2D imaging techniques, allowing for improved diagnostic accuracy, surgical planning, and minimally invasive interventions.

In 3D imaging, specialized software is used to reconstruct the acquired data into a volumetric model, which can be manipulated and viewed from different angles and perspectives. This enables healthcare professionals to better understand complex anatomical relationships, detect abnormalities, assess disease progression, and monitor treatment response. Common applications of 3D imaging include neuroimaging, orthopedic surgery planning, cancer staging, dental and maxillofacial reconstruction, and interventional radiology procedures.

DNA Sequence Analysis is the systematic determination of the order of nucleotides in a DNA molecule. It is a critical component of modern molecular biology, genetics, and genetic engineering. The process involves determining the exact order of the four nucleotide bases - adenine (A), guanine (G), cytosine (C), and thymine (T) - in a DNA molecule or fragment. This information is used in various applications such as identifying gene mutations, studying evolutionary relationships, developing molecular markers for breeding, and diagnosing genetic diseases.

The process of DNA Sequence Analysis typically involves several steps, including DNA extraction, PCR amplification (if necessary), purification, sequencing reaction, and electrophoresis. The resulting data is then analyzed using specialized software to determine the exact sequence of nucleotides.

In recent years, high-throughput DNA sequencing technologies have revolutionized the field of genomics, enabling the rapid and cost-effective sequencing of entire genomes. This has led to an explosion of genomic data and new insights into the genetic basis of many diseases and traits.

Image enhancement in the medical context refers to the process of improving the quality and clarity of medical images, such as X-rays, CT scans, MRI scans, or ultrasound images, to aid in the diagnosis and treatment of medical conditions. Image enhancement techniques may include adjusting contrast, brightness, or sharpness; removing noise or artifacts; or applying specialized algorithms to highlight specific features or structures within the image.

The goal of image enhancement is to provide clinicians with more accurate and detailed information about a patient's anatomy or physiology, which can help inform medical decision-making and improve patient outcomes.

I'm sorry for any confusion, but "Markov Chains" is a term from the field of mathematics and probability theory, not medicine. Markov Chains are mathematical systems that undergo transitions from one state to another according to certain probabilistic rules. They are named after Russian mathematician Andrey Markov. These chains are used in various fields, including computer science, physics, economics, and engineering, but not commonly in medical definitions or contexts.

Proteins are complex, large molecules that play critical roles in the body's functions. They are made up of amino acids, which are organic compounds that are the building blocks of proteins. Proteins are required for the structure, function, and regulation of the body's tissues and organs. They are essential for the growth, repair, and maintenance of body tissues, and they play a crucial role in many biological processes, including metabolism, immune response, and cellular signaling. Proteins can be classified into different types based on their structure and function, such as enzymes, hormones, antibodies, and structural proteins. They are found in various foods, especially animal-derived products like meat, dairy, and eggs, as well as plant-based sources like beans, nuts, and grains.

A protein database is a type of biological database that contains information about proteins and their structures, functions, sequences, and interactions with other molecules. These databases can include experimentally determined data, such as protein sequences derived from DNA sequencing or mass spectrometry, as well as predicted data based on computational methods.

Some examples of protein databases include:

1. UniProtKB: a comprehensive protein database that provides information about protein sequences, functions, and structures, as well as literature references and links to other resources.
2. PDB (Protein Data Bank): a database of three-dimensional protein structures determined by experimental methods such as X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy.
3. BLAST (Basic Local Alignment Search Tool): a web-based tool that allows users to compare a query protein sequence against a protein database to identify similar sequences and potential functional relationships.
4. InterPro: a database of protein families, domains, and functional sites that provides information about protein function based on sequence analysis and other data.
5. STRING (Search Tool for the Retrieval of Interacting Genes/Proteins): a database of known and predicted protein-protein interactions, including physical and functional associations.

Protein databases are essential tools in proteomics research, enabling researchers to study protein function, evolution, and interaction networks on a large scale.

Bayes' theorem, also known as Bayes' rule or Bayes' formula, is a fundamental principle in the field of statistics and probability theory. It describes how to update the probability of a hypothesis based on new evidence or data. The theorem is named after Reverend Thomas Bayes, who first formulated it in the 18th century.

In mathematical terms, Bayes' theorem states that the posterior probability of a hypothesis (H) given some observed evidence (E) is proportional to the product of the prior probability of the hypothesis (P(H)) and the likelihood of observing the evidence given the hypothesis (P(E|H)):

Posterior Probability = P(H|E) = [P(E|H) x P(H)] / P(E)

Where:

* P(H|E): The posterior probability of the hypothesis H after observing evidence E. This is the probability we want to calculate.
* P(E|H): The likelihood of observing evidence E given that the hypothesis H is true.
* P(H): The prior probability of the hypothesis H before observing any evidence.
* P(E): The marginal likelihood or probability of observing evidence E, regardless of whether the hypothesis H is true or not. This value can be calculated as the sum of the products of the likelihood and prior probability for all possible hypotheses: P(E) = Σ[P(E|Hi) x P(Hi)]

Bayes' theorem has many applications in various fields, including medicine, where it can be used to update the probability of a disease diagnosis based on test results or other clinical findings. It is also widely used in machine learning and artificial intelligence algorithms for probabilistic reasoning and decision making under uncertainty.

Gene expression profiling is a laboratory technique used to measure the activity (expression) of thousands of genes at once. This technique allows researchers and clinicians to identify which genes are turned on or off in a particular cell, tissue, or organism under specific conditions, such as during health, disease, development, or in response to various treatments.

The process typically involves isolating RNA from the cells or tissues of interest, converting it into complementary DNA (cDNA), and then using microarray or high-throughput sequencing technologies to determine which genes are expressed and at what levels. The resulting data can be used to identify patterns of gene expression that are associated with specific biological states or processes, providing valuable insights into the underlying molecular mechanisms of diseases and potential targets for therapeutic intervention.

In recent years, gene expression profiling has become an essential tool in various fields, including cancer research, drug discovery, and personalized medicine, where it is used to identify biomarkers of disease, predict patient outcomes, and guide treatment decisions.

I'm sorry for any confusion, but the "Monte Carlo method" is actually a term from the field of mathematics and computer science, not medicine. It refers to a statistical technique that allows for the modeling of complex systems by running multiple simulations with random inputs. This method is widely used in various fields such as physics, engineering, and finance, but it is not a medical concept or term.

Computer graphics is the field of study and practice related to creating images and visual content using computer technology. It involves various techniques, algorithms, and tools for generating, manipulating, and rendering digital images and models. These can include 2D and 3D modeling, animation, rendering, visualization, and image processing. Computer graphics is used in a wide range of applications, including video games, movies, scientific simulations, medical imaging, architectural design, and data visualization.

Automation in the medical context refers to the use of technology and programming to allow machines or devices to operate with minimal human intervention. This can include various types of medical equipment, such as laboratory analyzers, imaging devices, and robotic surgical systems. Automation can help improve efficiency, accuracy, and safety in healthcare settings by reducing the potential for human error and allowing healthcare professionals to focus on higher-level tasks. It is important to note that while automation has many benefits, it is also essential to ensure that appropriate safeguards are in place to prevent accidents and maintain quality of care.

A factual database in the medical context is a collection of organized and structured data that contains verified and accurate information related to medicine, healthcare, or health sciences. These databases serve as reliable resources for various stakeholders, including healthcare professionals, researchers, students, and patients, to access evidence-based information for making informed decisions and enhancing knowledge.

Examples of factual medical databases include:

1. PubMed: A comprehensive database of biomedical literature maintained by the US National Library of Medicine (NLM). It contains citations and abstracts from life sciences journals, books, and conference proceedings.
2. MEDLINE: A subset of PubMed, MEDLINE focuses on high-quality, peer-reviewed articles related to biomedicine and health. It is the primary component of the NLM's database and serves as a critical resource for healthcare professionals and researchers worldwide.
3. Cochrane Library: A collection of systematic reviews and meta-analyses focused on evidence-based medicine. The library aims to provide unbiased, high-quality information to support clinical decision-making and improve patient outcomes.
4. OVID: A platform that offers access to various medical and healthcare databases, including MEDLINE, Embase, and PsycINFO. It facilitates the search and retrieval of relevant literature for researchers, clinicians, and students.
5. ClinicalTrials.gov: A registry and results database of publicly and privately supported clinical studies conducted around the world. The platform aims to increase transparency and accessibility of clinical trial data for healthcare professionals, researchers, and patients.
6. UpToDate: An evidence-based, physician-authored clinical decision support resource that provides information on diagnosis, treatment, and prevention of medical conditions. It serves as a point-of-care tool for healthcare professionals to make informed decisions and improve patient care.
7. TRIP Database: A search engine designed to facilitate evidence-based medicine by providing quick access to high-quality resources, including systematic reviews, clinical guidelines, and practice recommendations.
8. National Guideline Clearinghouse (NGC): A database of evidence-based clinical practice guidelines and related documents developed through a rigorous review process. The NGC aims to provide clinicians, healthcare providers, and policymakers with reliable guidance for patient care.
9. DrugBank: A comprehensive, freely accessible online database containing detailed information about drugs, their mechanisms, interactions, and targets. It serves as a valuable resource for researchers, healthcare professionals, and students in the field of pharmacology and drug discovery.
10. Genetic Testing Registry (GTR): A database that provides centralized information about genetic tests, test developers, laboratories offering tests, and clinical validity and utility of genetic tests. It serves as a resource for healthcare professionals, researchers, and patients to make informed decisions regarding genetic testing.

Oligonucleotide Array Sequence Analysis is a type of microarray analysis that allows for the simultaneous measurement of the expression levels of thousands of genes in a single sample. In this technique, oligonucleotides (short DNA sequences) are attached to a solid support, such as a glass slide, in a specific pattern. These oligonucleotides are designed to be complementary to specific target mRNA sequences from the sample being analyzed.

During the analysis, labeled RNA or cDNA from the sample is hybridized to the oligonucleotide array. The level of hybridization is then measured and used to determine the relative abundance of each target sequence in the sample. This information can be used to identify differences in gene expression between samples, which can help researchers understand the underlying biological processes involved in various diseases or developmental stages.

It's important to note that this technique requires specialized equipment and bioinformatics tools for data analysis, as well as careful experimental design and validation to ensure accurate and reproducible results.

I'm sorry for any confusion, but "Numerical Analysis, Computer-Assisted" is not a commonly used medical term or concept. Numerical analysis is a branch of mathematics dealing with the approximation of problems by numerical values and the use of algorithms to solve these problems. It can be used in various fields, including medicine, for example, in modeling biological systems or analyzing medical data. However, "computer-assisted" generally refers to the use of computers to aid in a task, which is not exclusive to numerical analysis.

If you have any questions related to medicine or healthcare, I would be happy to try and help answer them!

The term "Theoretical Models" is used in various scientific fields, including medicine, to describe a representation of a complex system or phenomenon. It is a simplified framework that explains how different components of the system interact with each other and how they contribute to the overall behavior of the system. Theoretical models are often used in medical research to understand and predict the outcomes of diseases, treatments, or public health interventions.

A theoretical model can take many forms, such as mathematical equations, computer simulations, or conceptual diagrams. It is based on a set of assumptions and hypotheses about the underlying mechanisms that drive the system. By manipulating these variables and observing the effects on the model's output, researchers can test their assumptions and generate new insights into the system's behavior.

Theoretical models are useful for medical research because they allow scientists to explore complex systems in a controlled and systematic way. They can help identify key drivers of disease or treatment outcomes, inform the design of clinical trials, and guide the development of new interventions. However, it is important to recognize that theoretical models are simplifications of reality and may not capture all the nuances and complexities of real-world systems. Therefore, they should be used in conjunction with other forms of evidence, such as experimental data and observational studies, to inform medical decision-making.

A User-Computer Interface (also known as Human-Computer Interaction) refers to the point at which a person (user) interacts with a computer system. This can include both hardware and software components, such as keyboards, mice, touchscreens, and graphical user interfaces (GUIs). The design of the user-computer interface is crucial in determining the usability and accessibility of a computer system for the user. A well-designed interface should be intuitive, efficient, and easy to use, minimizing the cognitive load on the user and allowing them to effectively accomplish their tasks.

Data compression, in the context of medical informatics, refers to the process of encoding data to reduce its size while maintaining its integrity and accuracy. This technique is commonly used in transmitting and storing large datasets, such as medical images or genetic sequences, where smaller file sizes can significantly improve efficiency and speed up processing times.

There are two main types of data compression: lossless and lossy. Lossless compression ensures that the original data can be reconstructed exactly from the compressed data, making it essential for applications where data accuracy is critical, such as medical imaging or electronic health records. On the other hand, lossy compression involves discarding some redundant or less important data to achieve higher compression rates, but at the cost of reduced data quality.

In summary, data compression in a medical context refers to the process of reducing the size of digital data while maintaining its accuracy and integrity, which can improve efficiency in data transmission and storage.

I'm sorry for any confusion, but "Fuzzy Logic" is actually a term from the field of mathematics and computer science, not medicine. It is a form of many-valued logic that deals with reasoning that is approximate rather than fixed and exact. In contrast to traditional logic, where binary sets have distinct boundaries (true or false, 0 or 1, etc.), fuzzy logic allows for continuous values between 0 and 1, making it particularly useful in areas where precise definitions are difficult, such as medical diagnosis or robotics.

An artifact, in the context of medical terminology, refers to something that is created or introduced during a scientific procedure or examination that does not naturally occur in the patient or specimen being studied. Artifacts can take many forms and can be caused by various factors, including contamination, damage, degradation, or interference from equipment or external sources.

In medical imaging, for example, an artifact might appear as a distortion or anomaly on an X-ray, MRI, or CT scan that is not actually present in the patient's body. This can be caused by factors such as patient movement during the scan, metal implants or other foreign objects in the body, or issues with the imaging equipment itself.

Similarly, in laboratory testing, an artifact might refer to a substance or characteristic that is introduced into a sample during collection, storage, or analysis that can interfere with accurate results. This could include things like contamination from other samples, degradation of the sample over time, or interference from chemicals used in the testing process.

In general, artifacts are considered to be sources of error or uncertainty in medical research and diagnosis, and it is important to identify and account for them in order to ensure accurate and reliable results.

Computer-assisted diagnosis (CAD) is the use of computer systems to aid in the diagnostic process. It involves the use of advanced algorithms and data analysis techniques to analyze medical images, laboratory results, and other patient data to help healthcare professionals make more accurate and timely diagnoses. CAD systems can help identify patterns and anomalies that may be difficult for humans to detect, and they can provide second opinions and flag potential errors or uncertainties in the diagnostic process.

CAD systems are often used in conjunction with traditional diagnostic methods, such as physical examinations and patient interviews, to provide a more comprehensive assessment of a patient's health. They are commonly used in radiology, pathology, cardiology, and other medical specialties where imaging or laboratory tests play a key role in the diagnostic process.

While CAD systems can be very helpful in the diagnostic process, they are not infallible and should always be used as a tool to support, rather than replace, the expertise of trained healthcare professionals. It's important for medical professionals to use their clinical judgment and experience when interpreting CAD results and making final diagnoses.

A genetic database is a type of biomedical or health informatics database that stores and organizes genetic data, such as DNA sequences, gene maps, genotypes, haplotypes, and phenotype information. These databases can be used for various purposes, including research, clinical diagnosis, and personalized medicine.

There are different types of genetic databases, including:

1. Genomic databases: These databases store whole genome sequences, gene expression data, and other genomic information. Examples include the National Center for Biotechnology Information's (NCBI) GenBank, the European Nucleotide Archive (ENA), and the DNA Data Bank of Japan (DDBJ).
2. Gene databases: These databases contain information about specific genes, including their location, function, regulation, and evolution. Examples include the Online Mendelian Inheritance in Man (OMIM) database, the Universal Protein Resource (UniProt), and the Gene Ontology (GO) database.
3. Variant databases: These databases store information about genetic variants, such as single nucleotide polymorphisms (SNPs), insertions/deletions (INDELs), and copy number variations (CNVs). Examples include the Database of Single Nucleotide Polymorphisms (dbSNP), the Catalogue of Somatic Mutations in Cancer (COSMIC), and the International HapMap Project.
4. Clinical databases: These databases contain genetic and clinical information about patients, such as their genotype, phenotype, family history, and response to treatments. Examples include the ClinVar database, the Pharmacogenomics Knowledgebase (PharmGKB), and the Genetic Testing Registry (GTR).
5. Population databases: These databases store genetic information about different populations, including their ancestry, demographics, and genetic diversity. Examples include the 1000 Genomes Project, the Human Genome Diversity Project (HGDP), and the Allele Frequency Net Database (AFND).

Genetic databases can be publicly accessible or restricted to authorized users, depending on their purpose and content. They play a crucial role in advancing our understanding of genetics and genomics, as well as improving healthcare and personalized medicine.

Statistical data interpretation involves analyzing and interpreting numerical data in order to identify trends, patterns, and relationships. This process often involves the use of statistical methods and tools to organize, summarize, and draw conclusions from the data. The goal is to extract meaningful insights that can inform decision-making, hypothesis testing, or further research.

In medical contexts, statistical data interpretation is used to analyze and make sense of large sets of clinical data, such as patient outcomes, treatment effectiveness, or disease prevalence. This information can help healthcare professionals and researchers better understand the relationships between various factors that impact health outcomes, develop more effective treatments, and identify areas for further study.

Some common statistical methods used in data interpretation include descriptive statistics (e.g., mean, median, mode), inferential statistics (e.g., hypothesis testing, confidence intervals), and regression analysis (e.g., linear, logistic). These methods can help medical professionals identify patterns and trends in the data, assess the significance of their findings, and make evidence-based recommendations for patient care or public health policy.

Biological models, also known as physiological models or organismal models, are simplified representations of biological systems, processes, or mechanisms that are used to understand and explain the underlying principles and relationships. These models can be theoretical (conceptual or mathematical) or physical (such as anatomical models, cell cultures, or animal models). They are widely used in biomedical research to study various phenomena, including disease pathophysiology, drug action, and therapeutic interventions.

Examples of biological models include:

1. Mathematical models: These use mathematical equations and formulas to describe complex biological systems or processes, such as population dynamics, metabolic pathways, or gene regulation networks. They can help predict the behavior of these systems under different conditions and test hypotheses about their underlying mechanisms.
2. Cell cultures: These are collections of cells grown in a controlled environment, typically in a laboratory dish or flask. They can be used to study cellular processes, such as signal transduction, gene expression, or metabolism, and to test the effects of drugs or other treatments on these processes.
3. Animal models: These are living organisms, usually vertebrates like mice, rats, or non-human primates, that are used to study various aspects of human biology and disease. They can provide valuable insights into the pathophysiology of diseases, the mechanisms of drug action, and the safety and efficacy of new therapies.
4. Anatomical models: These are physical representations of biological structures or systems, such as plastic models of organs or tissues, that can be used for educational purposes or to plan surgical procedures. They can also serve as a basis for developing more sophisticated models, such as computer simulations or 3D-printed replicas.

Overall, biological models play a crucial role in advancing our understanding of biology and medicine, helping to identify new targets for therapeutic intervention, develop novel drugs and treatments, and improve human health.

To the best of my knowledge, "Normal Distribution" is not a term that has a specific medical definition. It is a statistical concept that describes a distribution of data points in which the majority of the data falls around a central value, with fewer and fewer data points appearing as you move further away from the center in either direction. This type of distribution is also known as a "bell curve" because of its characteristic shape.

In medical research, normal distribution may be used to describe the distribution of various types of data, such as the results of laboratory tests or patient outcomes. For example, if a large number of people are given a particular laboratory test, their test results might form a normal distribution, with most people having results close to the average and fewer people having results that are much higher or lower than the average.

It's worth noting that in some cases, data may not follow a normal distribution, and other types of statistical analyses may be needed to accurately describe and analyze the data.

'Information Storage and Retrieval' in the context of medical informatics refers to the processes and systems used for the recording, storing, organizing, protecting, and retrieving electronic health information (e.g., patient records, clinical data, medical images) for various purposes such as diagnosis, treatment planning, research, and education. This may involve the use of electronic health record (EHR) systems, databases, data warehouses, and other digital technologies that enable healthcare providers to access and share accurate, up-to-date, and relevant information about a patient's health status, medical history, and care plan. The goal is to improve the quality, safety, efficiency, and coordination of healthcare delivery by providing timely and evidence-based information to support clinical decision-making and patient engagement.

"Likelihood functions" is a statistical concept that is used in medical research and other fields to estimate the probability of obtaining a given set of data, given a set of assumptions or parameters. In other words, it is a function that describes how likely it is to observe a particular outcome or result, based on a set of model parameters.

More formally, if we have a statistical model that depends on a set of parameters θ, and we observe some data x, then the likelihood function is defined as:

L(θ | x) = P(x | θ)

This means that the likelihood function describes the probability of observing the data x, given a particular value of the parameter vector θ. By convention, the likelihood function is often expressed as a function of the parameters, rather than the data, so we might instead write:

L(θ) = P(x | θ)

The likelihood function can be used to estimate the values of the model parameters that are most consistent with the observed data. This is typically done by finding the value of θ that maximizes the likelihood function, which is known as the maximum likelihood estimator (MLE). The MLE has many desirable statistical properties, including consistency, efficiency, and asymptotic normality.

In medical research, likelihood functions are often used in the context of Bayesian analysis, where they are combined with prior distributions over the model parameters to obtain posterior distributions that reflect both the observed data and prior knowledge or assumptions about the parameter values. This approach is particularly useful when there is uncertainty or ambiguity about the true value of the parameters, as it allows researchers to incorporate this uncertainty into their analyses in a principled way.

Computer-assisted radiographic image interpretation is the use of computer algorithms and software to assist and enhance the interpretation and analysis of medical images produced by radiography, such as X-rays, CT scans, and MRI scans. The computer-assisted system can help identify and highlight certain features or anomalies in the image, such as tumors, fractures, or other abnormalities, which may be difficult for the human eye to detect. This technology can improve the accuracy and speed of diagnosis, and may also reduce the risk of human error. It's important to note that the final interpretation and diagnosis is always made by a qualified healthcare professional, such as a radiologist, who takes into account the computer-assisted analysis in conjunction with their clinical expertise and knowledge.

Genomics is the scientific study of genes and their functions. It involves the sequencing and analysis of an organism's genome, which is its complete set of DNA, including all of its genes. Genomics also includes the study of how genes interact with each other and with the environment. This field of study can provide important insights into the genetic basis of diseases and can lead to the development of new diagnostic tools and treatments.

I'm sorry for any confusion, but "Internet" is a term that pertains to the global network of interconnected computers and servers that enable the transmission and reception of data via the internet protocol (IP). It is not a medical term and does not have a specific medical definition. If you have any questions related to medicine or health, I'd be happy to try to help answer them for you!

A decision tree is a graphical representation of possible solutions to a decision based on certain conditions. It is a predictive modeling tool commonly used in statistics, data mining, and machine learning. In the medical field, decision trees can be used for clinical decision-making and predicting patient outcomes based on various factors such as symptoms, test results, or demographic information.

In a decision tree, each internal node represents a feature or attribute, and each branch represents a possible value or outcome of that feature. The leaves of the tree represent the final decisions or predictions. Decision trees are constructed by recursively partitioning the data into subsets based on the most significant attributes until a stopping criterion is met.

Decision trees can be used for both classification and regression tasks, making them versatile tools in medical research and practice. They can help healthcare professionals make informed decisions about patient care, identify high-risk patients, and develop personalized treatment plans. However, it's important to note that decision trees are only as good as the data they are trained on, and their accuracy may be affected by biases or limitations in the data.

Radiographic image enhancement refers to the process of improving the quality and clarity of radiographic images, such as X-rays, CT scans, or MRI images, through various digital techniques. These techniques may include adjusting contrast, brightness, and sharpness, as well as removing noise and artifacts that can interfere with image interpretation.

The goal of radiographic image enhancement is to provide medical professionals with clearer and more detailed images, which can help in the diagnosis and treatment of medical conditions. This process may be performed using specialized software or hardware tools, and it requires a strong understanding of imaging techniques and the specific needs of medical professionals.

The "subtraction technique" is not a widely recognized or established term in medical terminology. It may refer to various methods used in different medical contexts that involve subtracting or comparing measurements, values, or observations to diagnose, monitor, or treat medical conditions. However, without more specific context, it's difficult to provide an accurate medical definition of the term.

In radiology, for example, the subtraction technique is a method used in imaging to enhance the visibility of certain structures by digitally subtracting one image from another. This technique is often used in angiography to visualize blood vessels more clearly.

Therefore, it's essential to provide more context or specify the medical field when using the term "subtraction technique" to ensure accurate communication and understanding.

I'm afraid there seems to be a misunderstanding. Programming languages are a field of study in computer science and are not related to medicine. They are used to create computer programs, through the composition of symbols and words. Some popular programming languages include Python, Java, C++, and JavaScript. If you have any questions about programming or computer science, I'd be happy to try and help answer them!

Wavelet analysis is not a medical term, but rather a mathematical technique that has been applied in various fields, including medicine. It is a method used to analyze data signals or functions by decomposing them into different frequency components and time-shifted versions of the original signal. This allows for the examination of how the frequency content of a signal changes over time.

In the medical field, wavelet analysis has been applied in various ways such as:

1. Image processing: Wavelet analysis can be used to enhance medical images like MRI and CT scans by reducing noise while preserving important details.
2. Signal processing: It can be used to analyze physiological signals like ECG, EEG, and blood pressure waves to detect anomalies or patterns that may indicate diseases or conditions.
3. Data compression: Wavelet analysis is employed in the compression of large medical datasets, such as those generated by functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) scans.
4. Biomedical engineering: Wavelet analysis can be used to model and simulate complex biological systems, like the cardiovascular system or the nervous system.

In summary, wavelet analysis is a mathematical technique that has been applied in various medical fields for image processing, signal processing, data compression, and biomedical engineering purposes.

I'm happy to help! However, I believe there might be a bit of confusion in your question. "Computing methodologies" is a broad term that refers to the various approaches, techniques, and tools used to develop and implement computer systems, software, and solutions. It encompasses many different fields, including algorithms, data structures, programming languages, human-computer interaction, artificial intelligence, and more.

There isn't a specific medical definition for "computing methodologies" as it is not a term that is typically used within the medical field. However, if you have any specific questions about how computing methodologies are applied in healthcare or biomedical research, I would be happy to help! Just let me know what you're interested in learning more about.

Signal-to-Noise Ratio (SNR) is not a medical term per se, but it is widely used in various medical fields, particularly in diagnostic imaging and telemedicine. It is a measure from signal processing that compares the level of a desired signal to the level of background noise.

In the context of medical imaging (like MRI, CT scans, or ultrasound), a higher SNR means that the useful information (the signal) is stronger relative to the irrelevant and distracting data (the noise). This results in clearer, more detailed, and more accurate images, which can significantly improve diagnostic precision.

In telemedicine and remote patient monitoring, SNR is crucial for ensuring high-quality audio and video communication between healthcare providers and patients. A good SNR ensures that the transmitted data (voice or image) is received with minimal interference or distortion, enabling effective virtual consultations and diagnoses.

Data mining, in the context of health informatics and medical research, refers to the process of discovering patterns, correlations, and insights within large sets of patient or clinical data. It involves the use of advanced analytical techniques such as machine learning algorithms, statistical models, and artificial intelligence to identify and extract useful information from complex datasets.

The goal of data mining in healthcare is to support evidence-based decision making, improve patient outcomes, and optimize resource utilization. Applications of data mining in healthcare include predicting disease outbreaks, identifying high-risk patients, personalizing treatment plans, improving clinical workflows, and detecting fraud and abuse in healthcare systems.

Data mining can be performed on various types of healthcare data, including electronic health records (EHRs), medical claims databases, genomic data, imaging data, and sensor data from wearable devices. However, it is important to ensure that data mining techniques are used ethically and responsibly, with appropriate safeguards in place to protect patient privacy and confidentiality.

Protein interaction mapping is a research approach used to identify and characterize the physical interactions between different proteins within a cell or organism. This process often involves the use of high-throughput experimental techniques, such as yeast two-hybrid screening, mass spectrometry-based approaches, or protein fragment complementation assays, to detect and quantify the binding affinities of protein pairs. The resulting data is then used to construct a protein interaction network, which can provide insights into functional relationships between proteins, help elucidate cellular pathways, and inform our understanding of biological processes in health and disease.

Molecular models are three-dimensional representations of molecular structures that are used in the field of molecular biology and chemistry to visualize and understand the spatial arrangement of atoms and bonds within a molecule. These models can be physical or computer-generated and allow researchers to study the shape, size, and behavior of molecules, which is crucial for understanding their function and interactions with other molecules.

Physical molecular models are often made up of balls (representing atoms) connected by rods or sticks (representing bonds). These models can be constructed manually using materials such as plastic or wooden balls and rods, or they can be created using 3D printing technology.

Computer-generated molecular models, on the other hand, are created using specialized software that allows researchers to visualize and manipulate molecular structures in three dimensions. These models can be used to simulate molecular interactions, predict molecular behavior, and design new drugs or chemicals with specific properties. Overall, molecular models play a critical role in advancing our understanding of molecular structures and their functions.

I'm sorry for any confusion, but "Wireless Technology" is not a term that has a specific medical definition. Wireless technology generally refers to the transmission of information over a distance without the use of electrical conductors or "wires." It encompasses a wide range of technologies, including cellular networks, Wi-Fi, Bluetooth, and various forms of telecommunication.

However, in a medical context, wireless technology can be used to refer to devices or systems that transmit patient data without the need for physical connections. For example, wireless pulse oximeters, blood glucose monitors, or cardiac event monitors. These devices use wireless technologies to send patient data to a remote monitoring station or to a healthcare provider's electronic health record system. This can provide more flexibility and mobility for patients, and can also improve the efficiency of healthcare delivery.

Support Vector Machines (SVM) is not a medical term, but a concept in machine learning, a branch of artificial intelligence. SVM is used in various fields including medicine for data analysis and pattern recognition. Here's a brief explanation of SVM:

Support Vector Machines is a supervised learning algorithm which analyzes data and recognizes patterns, used for classification and regression analysis. The goal of SVM is to find the optimal boundary or hyperplane that separates data into different classes with the maximum margin. This margin is the distance between the hyperplane and the nearest data points, also known as support vectors. By finding this optimal boundary, SVM can effectively classify new data points.

In the context of medical research, SVM has been used for various applications such as:

* Classifying medical images (e.g., distinguishing between cancerous and non-cancerous tissues)
* Predicting patient outcomes based on clinical or genetic data
* Identifying biomarkers associated with diseases
* Analyzing electronic health records to predict disease risk or treatment response

Therefore, while SVM is not a medical term per se, it is an important tool in the field of medical informatics and bioinformatics.

Automatic Data Processing (ADP) is not a medical term, but a general business term that refers to the use of computers and software to automate and streamline administrative tasks and processes. In a medical context, ADP may be used in healthcare settings to manage electronic health records (EHRs), billing and coding, insurance claims processing, and other data-intensive tasks.

The goal of using ADP in healthcare is to improve efficiency, accuracy, and timeliness of administrative processes, while reducing costs and errors associated with manual data entry and management. By automating these tasks, healthcare providers can focus more on patient care and less on paperwork, ultimately improving the quality of care delivered to patients.

I must clarify that there is no specific medical definition for "Software Design." Software design is a term used in the field of software engineering and development, which includes the creation of detailed plans, schemas, and models that describe how a software system or application should be constructed and implemented. This process involves various activities such as defining the architecture, components, modules, interfaces, data structures, and algorithms required to build the software system.

However, in the context of medical software or healthcare applications, software design would still refer to the planning and structuring of the software system but with a focus on addressing specific needs and challenges within the medical domain. This might include considerations for data privacy and security, regulatory compliance (such as HIPAA or GDPR), integration with existing health IT systems, user experience (UX) design for healthcare professionals and patients, and evidence-based decision support features.

RNA Sequence Analysis is a branch of bioinformatics that involves the determination and analysis of the nucleotide sequence of Ribonucleic Acid (RNA) molecules. This process includes identifying and characterizing the individual RNA molecules, determining their functions, and studying their evolutionary relationships.

RNA Sequence Analysis typically involves the use of high-throughput sequencing technologies to generate large datasets of RNA sequences, which are then analyzed using computational methods. The analysis may include comparing the sequences to reference databases to identify known RNA molecules or discovering new ones, identifying patterns and features in the sequences, such as motifs or domains, and predicting the secondary and tertiary structures of the RNA molecules.

RNA Sequence Analysis has many applications in basic research, including understanding gene regulation, identifying novel non-coding RNAs, and studying evolutionary relationships between organisms. It also has practical applications in clinical settings, such as diagnosing and monitoring diseases, developing new therapies, and personalized medicine.

A computer is a programmable electronic device that can store, retrieve, and process data. It is composed of several components including:

1. Hardware: The physical components of a computer such as the central processing unit (CPU), memory (RAM), storage devices (hard drive or solid-state drive), and input/output devices (monitor, keyboard, and mouse).
2. Software: The programs and instructions that are used to perform specific tasks on a computer. This includes operating systems, applications, and utilities.
3. Input: Devices or methods used to enter data into a computer, such as a keyboard, mouse, scanner, or digital camera.
4. Processing: The function of the CPU in executing instructions and performing calculations on data.
5. Output: The results of processing, which can be displayed on a monitor, printed on paper, or saved to a storage device.

Computers come in various forms and sizes, including desktop computers, laptops, tablets, and smartphones. They are used in a wide range of applications, from personal use for communication, entertainment, and productivity, to professional use in fields such as medicine, engineering, finance, and education.

Molecular sequence data refers to the specific arrangement of molecules, most commonly nucleotides in DNA or RNA, or amino acids in proteins, that make up a biological macromolecule. This data is generated through laboratory techniques such as sequencing, and provides information about the exact order of the constituent molecules. This data is crucial in various fields of biology, including genetics, evolution, and molecular biology, allowing for comparisons between different organisms, identification of genetic variations, and studies of gene function and regulation.

I'm sorry for any confusion, but "Stochastic Processes" is actually a term from the field of mathematics, specifically probability theory and statistics. It doesn't have a direct medical definition.

However, to provide some context that might be relevant if you're studying a medical field with a strong statistical component: A stochastic process is a mathematical model used to describe random systems that evolve over time. It consists of a set of random variables indexed by time or some other parameter. The values of these variables at different times or parameters are not independent, but rather depend on each other in ways described by probability distributions.

In medical research, stochastic processes might be used to model the spread of a disease through a population over time, or the changing health status of an individual patient over the course of their treatment. However, it's important to note that this is a high-level overview and the specific use of stochastic processes in medical research would depend on the particular application.

A genome is the complete set of genetic material (DNA, or in some viruses, RNA) present in a single cell of an organism. It includes all of the genes, both coding and noncoding, as well as other regulatory elements that together determine the unique characteristics of that organism. The human genome, for example, contains approximately 3 billion base pairs and about 20,000-25,000 protein-coding genes.

The term "genome" was first coined by Hans Winkler in 1920, derived from the word "gene" and the suffix "-ome," which refers to a complete set of something. The study of genomes is known as genomics.

Understanding the genome can provide valuable insights into the genetic basis of diseases, evolution, and other biological processes. With advancements in sequencing technologies, it has become possible to determine the entire genomic sequence of many organisms, including humans, and use this information for various applications such as personalized medicine, gene therapy, and biotechnology.

Gene Regulatory Networks (GRNs) are complex systems of molecular interactions that regulate the expression of genes within an organism. These networks consist of various types of regulatory elements, including transcription factors, enhancers, promoters, and silencers, which work together to control when, where, and to what extent a gene is expressed.

In GRNs, transcription factors bind to specific DNA sequences in the regulatory regions of target genes, either activating or repressing their transcription into messenger RNA (mRNA). This process is influenced by various intracellular and extracellular signals that modulate the activity of transcription factors, allowing for precise regulation of gene expression in response to changing environmental conditions.

The structure and behavior of GRNs can be represented as a network of nodes (genes) and edges (regulatory interactions), with the strength and directionality of these interactions determined by the specific molecular mechanisms involved. Understanding the organization and dynamics of GRNs is crucial for elucidating the underlying causes of various biological processes, including development, differentiation, homeostasis, and disease.

A Receiver Operating Characteristic (ROC) curve is a graphical representation used in medical decision-making and statistical analysis to illustrate the performance of a binary classifier system, such as a diagnostic test or a machine learning algorithm. It's a plot that shows the tradeoff between the true positive rate (sensitivity) and the false positive rate (1 - specificity) for different threshold settings.

The x-axis of an ROC curve represents the false positive rate (the proportion of negative cases incorrectly classified as positive), while the y-axis represents the true positive rate (the proportion of positive cases correctly classified as positive). Each point on the curve corresponds to a specific decision threshold, with higher points indicating better performance.

The area under the ROC curve (AUC) is a commonly used summary measure that reflects the overall performance of the classifier. An AUC value of 1 indicates perfect discrimination between positive and negative cases, while an AUC value of 0.5 suggests that the classifier performs no better than chance.

ROC curves are widely used in healthcare to evaluate diagnostic tests, predictive models, and screening tools for various medical conditions, helping clinicians make informed decisions about patient care based on the balance between sensitivity and specificity.

Equipment design, in the medical context, refers to the process of creating and developing medical equipment and devices, such as surgical instruments, diagnostic machines, or assistive technologies. This process involves several stages, including:

1. Identifying user needs and requirements
2. Concept development and brainstorming
3. Prototyping and testing
4. Design for manufacturing and assembly
5. Safety and regulatory compliance
6. Verification and validation
7. Training and support

The goal of equipment design is to create safe, effective, and efficient medical devices that meet the needs of healthcare providers and patients while complying with relevant regulations and standards. The design process typically involves a multidisciplinary team of engineers, clinicians, designers, and researchers who work together to develop innovative solutions that improve patient care and outcomes.

A chemical model is a simplified representation or description of a chemical system, based on the laws of chemistry and physics. It is used to explain and predict the behavior of chemicals and chemical reactions. Chemical models can take many forms, including mathematical equations, diagrams, and computer simulations. They are often used in research, education, and industry to understand complex chemical processes and develop new products and technologies.

For example, a chemical model might be used to describe the way that atoms and molecules interact in a particular reaction, or to predict the properties of a new material. Chemical models can also be used to study the behavior of chemicals at the molecular level, such as how they bind to each other or how they are affected by changes in temperature or pressure.

It is important to note that chemical models are simplifications of reality and may not always accurately represent every aspect of a chemical system. They should be used with caution and validated against experimental data whenever possible.

In the context of medicine and healthcare, 'probability' does not have a specific medical definition. However, in general terms, probability is a branch of mathematics that deals with the study of numerical quantities called probabilities, which are assigned to events or sets of events. Probability is a measure of the likelihood that an event will occur. It is usually expressed as a number between 0 and 1, where 0 indicates that the event is impossible and 1 indicates that the event is certain to occur.

In medical research and statistics, probability is often used to quantify the uncertainty associated with statistical estimates or hypotheses. For example, a p-value is a probability that measures the strength of evidence against a hypothesis. A small p-value (typically less than 0.05) suggests that the observed data are unlikely under the assumption of the null hypothesis, and therefore provides evidence in favor of an alternative hypothesis.

Probability theory is also used to model complex systems and processes in medicine, such as disease transmission dynamics or the effectiveness of medical interventions. By quantifying the uncertainty associated with these models, researchers can make more informed decisions about healthcare policies and practices.

The Predictive Value of Tests, specifically the Positive Predictive Value (PPV) and Negative Predictive Value (NPV), are measures used in diagnostic tests to determine the probability that a positive or negative test result is correct.

Positive Predictive Value (PPV) is the proportion of patients with a positive test result who actually have the disease. It is calculated as the number of true positives divided by the total number of positive results (true positives + false positives). A higher PPV indicates that a positive test result is more likely to be a true positive, and therefore the disease is more likely to be present.

Negative Predictive Value (NPV) is the proportion of patients with a negative test result who do not have the disease. It is calculated as the number of true negatives divided by the total number of negative results (true negatives + false negatives). A higher NPV indicates that a negative test result is more likely to be a true negative, and therefore the disease is less likely to be present.

The predictive value of tests depends on the prevalence of the disease in the population being tested, as well as the sensitivity and specificity of the test. A test with high sensitivity and specificity will generally have higher predictive values than a test with low sensitivity and specificity. However, even a highly sensitive and specific test can have low predictive values if the prevalence of the disease is low in the population being tested.

Chromosome mapping, also known as physical mapping, is the process of determining the location and order of specific genes or genetic markers on a chromosome. This is typically done by using various laboratory techniques to identify landmarks along the chromosome, such as restriction enzyme cutting sites or patterns of DNA sequence repeats. The resulting map provides important information about the organization and structure of the genome, and can be used for a variety of purposes, including identifying the location of genes associated with genetic diseases, studying evolutionary relationships between organisms, and developing genetic markers for use in breeding or forensic applications.

Phylogeny is the evolutionary history and relationship among biological entities, such as species or genes, based on their shared characteristics. In other words, it refers to the branching pattern of evolution that shows how various organisms have descended from a common ancestor over time. Phylogenetic analysis involves constructing a tree-like diagram called a phylogenetic tree, which depicts the inferred evolutionary relationships among organisms or genes based on molecular sequence data or other types of characters. This information is crucial for understanding the diversity and distribution of life on Earth, as well as for studying the emergence and spread of diseases.

Medical Definition:

Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic imaging technique that uses a strong magnetic field and radio waves to create detailed cross-sectional or three-dimensional images of the internal structures of the body. The patient lies within a large, cylindrical magnet, and the scanner detects changes in the direction of the magnetic field caused by protons in the body. These changes are then converted into detailed images that help medical professionals to diagnose and monitor various medical conditions, such as tumors, injuries, or diseases affecting the brain, spinal cord, heart, blood vessels, joints, and other internal organs. MRI does not use radiation like computed tomography (CT) scans.

In the field of medicine, "time factors" refer to the duration of symptoms or time elapsed since the onset of a medical condition, which can have significant implications for diagnosis and treatment. Understanding time factors is crucial in determining the progression of a disease, evaluating the effectiveness of treatments, and making critical decisions regarding patient care.

For example, in stroke management, "time is brain," meaning that rapid intervention within a specific time frame (usually within 4.5 hours) is essential to administering tissue plasminogen activator (tPA), a clot-busting drug that can minimize brain damage and improve patient outcomes. Similarly, in trauma care, the "golden hour" concept emphasizes the importance of providing definitive care within the first 60 minutes after injury to increase survival rates and reduce morbidity.

Time factors also play a role in monitoring the progression of chronic conditions like diabetes or heart disease, where regular follow-ups and assessments help determine appropriate treatment adjustments and prevent complications. In infectious diseases, time factors are crucial for initiating antibiotic therapy and identifying potential outbreaks to control their spread.

Overall, "time factors" encompass the significance of recognizing and acting promptly in various medical scenarios to optimize patient outcomes and provide effective care.

A base sequence in the context of molecular biology refers to the specific order of nucleotides in a DNA or RNA molecule. In DNA, these nucleotides are adenine (A), guanine (G), cytosine (C), and thymine (T). In RNA, uracil (U) takes the place of thymine. The base sequence contains genetic information that is transcribed into RNA and ultimately translated into proteins. It is the exact order of these bases that determines the genetic code and thus the function of the DNA or RNA molecule.

Discriminant analysis is a statistical method used for classifying observations or individuals into distinct categories or groups based on multiple predictor variables. It is commonly used in medical research to help diagnose or predict the presence or absence of a particular condition or disease.

In discriminant analysis, a linear combination of the predictor variables is created, and the resulting function is used to determine the group membership of each observation. The function is derived from the means and variances of the predictor variables for each group, with the goal of maximizing the separation between the groups while minimizing the overlap.

There are two types of discriminant analysis:

1. Linear Discriminant Analysis (LDA): This method assumes that the predictor variables are normally distributed and have equal variances within each group. LDA is used when there are two or more groups to be distinguished.
2. Quadratic Discriminant Analysis (QDA): This method does not assume equal variances within each group, allowing for more flexibility in modeling the distribution of predictor variables. QDA is used when there are two or more groups to be distinguished.

Discriminant analysis can be useful in medical research for developing diagnostic models that can accurately classify patients based on a set of clinical or laboratory measures. It can also be used to identify which predictor variables are most important in distinguishing between different groups, providing insights into the underlying biological mechanisms of disease.

Cone-beam computed tomography (CBCT) is a medical imaging technique that uses a cone-shaped X-ray beam to create detailed, cross-sectional images of the body. In dental and maxillofacial radiology, CBCT is used to produce three-dimensional images of the teeth, jaws, and surrounding bones.

CBCT differs from traditional computed tomography (CT) in that it uses a cone-shaped X-ray beam instead of a fan-shaped beam, which allows for a faster scan time and lower radiation dose. The X-ray beam is rotated around the patient's head, capturing data from multiple angles, which is then reconstructed into a three-dimensional image using specialized software.

CBCT is commonly used in dental implant planning, orthodontic treatment planning, airway analysis, and the diagnosis and management of jaw pathologies such as tumors and fractures. It provides detailed information about the anatomy of the teeth, jaws, and surrounding structures, which can help clinicians make more informed decisions about patient care.

However, it is important to note that CBCT should only be used when necessary, as it still involves exposure to ionizing radiation. The benefits of using CBCT must be weighed against the potential risks associated with radiation exposure.

X-ray computed tomography (CT or CAT scan) is a medical imaging method that uses computer-processed combinations of many X-ray images taken from different angles to produce cross-sectional (tomographic) images (virtual "slices") of the body. These cross-sectional images can then be used to display detailed internal views of organs, bones, and soft tissues in the body.

The term "computed tomography" is used instead of "CT scan" or "CAT scan" because the machines take a series of X-ray measurements from different angles around the body and then use a computer to process these data to create detailed images of internal structures within the body.

CT scanning is a noninvasive, painless medical test that helps physicians diagnose and treat medical conditions. CT imaging provides detailed information about many types of tissue including lung, bone, soft tissue and blood vessels. CT examinations can be performed on every part of the body for a variety of reasons including diagnosis, surgical planning, and monitoring of therapeutic responses.

In computed tomography (CT), an X-ray source and detector rotate around the patient, measuring the X-ray attenuation at many different angles. A computer uses this data to construct a cross-sectional image by the process of reconstruction. This technique is called "tomography". The term "computed" refers to the use of a computer to reconstruct the images.

CT has become an important tool in medical imaging and diagnosis, allowing radiologists and other physicians to view detailed internal images of the body. It can help identify many different medical conditions including cancer, heart disease, lung nodules, liver tumors, and internal injuries from trauma. CT is also commonly used for guiding biopsies and other minimally invasive procedures.

In summary, X-ray computed tomography (CT or CAT scan) is a medical imaging technique that uses computer-processed combinations of many X-ray images taken from different angles to produce cross-sectional images of the body. It provides detailed internal views of organs, bones, and soft tissues in the body, allowing physicians to diagnose and treat medical conditions.

Least-Squares Analysis is not a medical term, but rather a statistical method that is used in various fields including medicine. It is a way to find the best fit line or curve for a set of data points by minimizing the sum of the squared distances between the observed data points and the fitted line or curve. This method is often used in medical research to analyze data, such as fitting a regression line to a set of data points to make predictions or identify trends. The goal is to find the line or curve that most closely represents the pattern of the data, which can help researchers understand relationships between variables and make more informed decisions based on their analysis.

"Nonlinear dynamics is a branch of mathematics and physics that deals with the study of systems that exhibit nonlinear behavior, where the output is not directly proportional to the input. In the context of medicine, nonlinear dynamics can be used to model complex biological systems such as the human cardiovascular system or the brain, where the interactions between different components can lead to emergent properties and behaviors that are difficult to predict using traditional linear methods. Nonlinear dynamic models can help to understand the underlying mechanisms of these systems, make predictions about their behavior, and develop interventions to improve health outcomes."

I'm sorry for any confusion, but "Programming, Linear" is not a recognized medical term or concept. The term "linear programming" comes from the field of mathematics and computer science, and it refers to a method used to find the optimal solution(s) to a problem with certain constraints, where the objective function and constraints are linear.

If you have any questions related to medicine or healthcare, I'd be happy to try to help answer them for you!

Equipment Failure Analysis is a process of identifying the cause of failure in medical equipment or devices. This involves a systematic examination and evaluation of the equipment, its components, and operational history to determine why it failed. The analysis may include physical inspection, chemical testing, and review of maintenance records, as well as assessment of design, manufacturing, and usage factors that may have contributed to the failure.

The goal of Equipment Failure Analysis is to identify the root cause of the failure, so that corrective actions can be taken to prevent similar failures in the future. This is important in medical settings to ensure patient safety and maintain the reliability and effectiveness of medical equipment.

A human genome is the complete set of genetic information contained within the 23 pairs of chromosomes found in the nucleus of most human cells. It includes all of the genes, which are segments of DNA that contain the instructions for making proteins, as well as non-coding regions of DNA that regulate gene expression and provide structural support to the chromosomes.

The human genome contains approximately 3 billion base pairs of DNA and is estimated to contain around 20,000-25,000 protein-coding genes. The sequencing of the human genome was completed in 2003 as part of the Human Genome Project, which has had a profound impact on our understanding of human biology, disease, and evolution.

Proteomics is the large-scale study and analysis of proteins, including their structures, functions, interactions, modifications, and abundance, in a given cell, tissue, or organism. It involves the identification and quantification of all expressed proteins in a biological sample, as well as the characterization of post-translational modifications, protein-protein interactions, and functional pathways. Proteomics can provide valuable insights into various biological processes, diseases, and drug responses, and has applications in basic research, biomedicine, and clinical diagnostics. The field combines various techniques from molecular biology, chemistry, physics, and bioinformatics to study proteins at a systems level.

A nucleic acid database is a type of biological database that contains sequence, structure, and functional information about nucleic acids, such as DNA and RNA. These databases are used in various fields of biology, including genomics, molecular biology, and bioinformatics, to store, search, and analyze nucleic acid data.

Some common types of nucleic acid databases include:

1. Nucleotide sequence databases: These databases contain the primary nucleotide sequences of DNA and RNA molecules from various organisms. Examples include GenBank, EMBL-Bank, and DDBJ.
2. Structure databases: These databases contain three-dimensional structures of nucleic acids determined by experimental methods such as X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. Examples include the Protein Data Bank (PDB) and the Nucleic Acid Database (NDB).
3. Functional databases: These databases contain information about the functions of nucleic acids, such as their roles in gene regulation, transcription, and translation. Examples include the Gene Ontology (GO) database and the RegulonDB.
4. Genome databases: These databases contain genomic data for various organisms, including whole-genome sequences, gene annotations, and genetic variations. Examples include the Human Genome Database (HGD) and the Ensembl Genome Browser.
5. Comparative databases: These databases allow for the comparison of nucleic acid sequences or structures across different species or conditions. Examples include the Comparative RNA Web (CRW) Site and the Sequence Alignment and Modeling (SAM) system.

Nucleic acid databases are essential resources for researchers to study the structure, function, and evolution of nucleic acids, as well as to develop new tools and methods for analyzing and interpreting nucleic acid data.

Principal Component Analysis (PCA) is not a medical term, but a statistical technique that is used in various fields including bioinformatics and medicine. It is a method used to identify patterns in high-dimensional data by reducing the dimensionality of the data while retaining most of the variation in the dataset.

In medical or biological research, PCA may be used to analyze large datasets such as gene expression data or medical imaging data. By applying PCA, researchers can identify the principal components, which are linear combinations of the original variables that explain the maximum amount of variance in the data. These principal components can then be used for further analysis, visualization, and interpretation of the data.

PCA is a widely used technique in data analysis and has applications in various fields such as genomics, proteomics, metabolomics, and medical imaging. It helps researchers to identify patterns and relationships in complex datasets, which can lead to new insights and discoveries in medical research.

Single Nucleotide Polymorphism (SNP) is a type of genetic variation that occurs when a single nucleotide (A, T, C, or G) in the DNA sequence is altered. This alteration must occur in at least 1% of the population to be considered a SNP. These variations can help explain why some people are more susceptible to certain diseases than others and can also influence how an individual responds to certain medications. SNPs can serve as biological markers, helping scientists locate genes that are associated with disease. They can also provide information about an individual's ancestry and ethnic background.

An amino acid sequence is the specific order of amino acids in a protein or peptide molecule, formed by the linking of the amino group (-NH2) of one amino acid to the carboxyl group (-COOH) of another amino acid through a peptide bond. The sequence is determined by the genetic code and is unique to each type of protein or peptide. It plays a crucial role in determining the three-dimensional structure and function of proteins.

Computer communication networks (CCN) refer to the interconnected systems or groups of computers that are able to communicate and share resources and information with each other. These networks may be composed of multiple interconnected devices, including computers, servers, switches, routers, and other hardware components. The connections between these devices can be established through various types of media, such as wired Ethernet cables or wireless Wi-Fi signals.

CCNs enable the sharing of data, applications, and services among users and devices, and they are essential for supporting modern digital communication and collaboration. Some common examples of CCNs include local area networks (LANs), wide area networks (WANs), and the Internet. These networks can be designed and implemented in various topologies, such as star, ring, bus, mesh, and tree configurations, to meet the specific needs and requirements of different organizations and applications.

I'm sorry for any confusion, but "Natural Language Processing" (NLP) is actually a subfield of artificial intelligence that focuses on the interaction between computers and human language. It involves developing algorithms and software to understand, interpret, and generate human language in a valuable way.

In a medical context, NLP can be used to analyze electronic health records, clinical notes, and other forms of medical documentation to extract meaningful information, support clinical decision-making, and improve patient care. For example, NLP can help identify patients at risk for certain conditions, monitor treatment responses, and detect adverse drug events.

However, NLP is not a medical term or concept itself, so it doesn't have a specific medical definition.

Tomography is a medical imaging technique used to produce cross-sectional images or slices of specific areas of the body. This technique uses various forms of radiation (X-rays, gamma rays) or sound waves (ultrasound) to create detailed images of the internal structures, such as organs, bones, and tissues. Common types of tomography include Computerized Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI). The primary advantage of tomography is its ability to provide clear and detailed images of internal structures, allowing healthcare professionals to accurately diagnose and monitor a wide range of medical conditions.

The proteome is the entire set of proteins produced or present in an organism, system, organ, or cell at a certain time under specific conditions. It is a dynamic collection of protein species that changes over time, responding to various internal and external stimuli such as disease, stress, or environmental factors. The study of the proteome, known as proteomics, involves the identification and quantification of these protein components and their post-translational modifications, providing valuable insights into biological processes, functional pathways, and disease mechanisms.

Students' alternative algorithms are often just as correct, efficient, and generalizable as the standard algorithms, and ... In elementary arithmetic, a standard algorithm or method is a specific method of computation which is conventionally taught for ... something that is usually lost in the memorization of standard algorithms). The development of sophisticated calculators has ...
"Source details: Algorithms". Scopus preview. Elsevier. Retrieved 30 July 2018. "Algorithms". zbMATH Open. Springer Science+ ... Algorithms. 1 (1): 1. doi:10.3390/a1010001. "Algorithms". 2022 Journal Citation Reports. Web of Science (Science ed.). ... ACM Transactions on Algorithms Algorithmica Journal of Algorithms (Elsevier) Iwama, Kazuo (2008). "Editor's Foreword". ... Algorithms is a monthly peer-reviewed open-access scientific journal of mathematics, covering design, analysis, and experiments ...
For instance, all XDAIS compliant algorithms must implement an Algorithm Interface, called IALG. For those algorithms utilizing ... XDAIS or eXpressDsp Algorithm Interoperability Standard is a standard for algorithm development by Texas Instruments for the ... Problems are often caused in algorithm by hard-coding access to system resources that are used by other algorithms. XDAIS ... The XDAIS standard address the issues of algorithm resource allocation and consumption on a DSP. Algorithms that comply with ...
For n ≥ 2 observations DeWit/USNO Nautical Almanac/Compac Data, Least squares algorithm for n LOPs Kaplan algorithm, USNO. For ... The navigational algorithms are the quintessence of the executable software on portable calculators or Smartphone as an aid to ... Algorithm implementation: For n = 2 observations An analytical solution of the two star sight problem of celestial navigation, ... Calculators (and the like) do not need books (they have tables and ephemeris integrated) and, with their own algorithms, allow ...
... is a book by Thomas H. Cormen about the basic principles and applications of computer algorithms. The book ... "Algorithms Unlocked". MIT Press. Retrieved April 30, 2015. MIT Press: Algorithms Unlocked v t e (2013 non-fiction books, ... consists of ten chapters, and deals with the topics of searching, sorting, basic graph algorithms, string processing, the ...
... is a text based on over six years of academic research on Google search algorithms, examining search ... Algorithms of Oppression: How Search Engines Reinforce Racism, by Safiya Umoja Noble , Booklist Online. Algorithms of ... ALGORITHMS OF OPPRESSION , Kirkus Reviews. Erigha, Maryann (2019-07-01). "Algorithms of Oppression: How Search Engines ... Google hides behind their algorithm that has been proven to perpetuate inequalities. In Chapter 3 of Algorithms of Oppression, ...
There is a concept of algorithm certification emerging as a method of regulating algorithms. Algorithm certification involves ... One of those sellers used an algorithm which essentially matched its rival's price. That rival had an algorithm which always ... The motivation for regulation of algorithms is the apprehension of losing control over the algorithms, whose impact on human ... algorithms assigning exam grades to students in the UK sparked open protest under the banner "Fuck the algorithm." This protest ...
A much simpler algorithm was developed by Chan in 1996, and is called Chan's algorithm. Known convex hull algorithms are listed ... Such algorithms are called output-sensitive algorithms. They may be asymptotically more efficient than Θ(n log n) algorithms in ... This algorithm is also applicable to the three dimensional case. Monotone chain, a.k.a. Andrew's algorithm- O(n log n) ... A number of algorithms are known for the three-dimensional case, as well as for arbitrary dimensions. Chan's algorithm is used ...
... (AAD) is the use of specific algorithms-editors to assist in the creation, modification, analysis, or ... The algorithms-editors are usually integrated with 3D modeling packages and read several programming languages, both scripted ... The acronym appears for the first time in the book AAD Algorithms-Aided Design, Parametric Strategies using Grasshopper, ... or visual (RhinoScript, Grasshopper, MEL, C#, Python). The Algorithms-Aided Design allows designers to overcome the limitations ...
... : Linkages, Origami, Polyhedra is a monograph on the mathematics and computational geometry of ... Carbno, Collin (May 2009), "Review of Geometric Folding Algorithms", MAA Reviews, Mathematical Association of America Paquete, ... "Review of Geometric Folding Algorithms", EMS Reviews, European Mathematical Society Fasy, Brittany Terese; Millman, David L. ( ... web site for Geometric Folding Algorithms including contents, errata, and advances on open problems (Linkages (mechanical), ...
These constructions are based on recursive algorithms. Kleitman and Wang gave these algorithms in 1973. The algorithm is based ... The Kleitman-Wang algorithms are two different algorithms in graph theory solving the digraph realization problem, i.e. the ... The algorithm is based on the following theorem. Let S = ( ( a 1 , b 1 ) , … , ( a n , b n ) ) {\displaystyle S=((a_{1},b_{1 ... In each step of the algorithm one constructs the arcs of a digraph with vertices v 1 , … , v n {\displaystyle v_{1},\dots ,v_{n ...
Algorithm Maekawa's Algorithm Raymond's Algorithm Ricart-Agrawala Algorithm Snapshot algorithm: record a consistent global ... algorithms for the constraint satisfaction AC-3 algorithm Difference map algorithm Min conflicts algorithm Chaff algorithm: an ... Xiaolin Wu's line algorithm: algorithm for line antialiasing. Midpoint circle algorithm: an algorithm used to determine the ... 1 algorithm Pollard's rho algorithm prime factorization algorithm Quadratic sieve Shor's algorithm Special number field sieve ...
The algorithm (and therefore the program code) is simpler than other algorithms, especially compared to strong algorithms that ... An algorithm combining a constraint-model-based algorithm with backtracking would have the advantage of fast solving time - of ... Modelling Sudoku as an exact cover problem and using an algorithm such as Knuth's Algorithm X and his Dancing Links technique " ... The simplex algorithm is able to solve proper Sudokus, indicating if the Sudoku is not valid (no solution). If there is more ...
"Introduction to Algorithms, Third Edition". www.cs.dartmouth.edu. "Errata for Introduction to Algorithms, 4th Edition". mitp- ... 31 Number-Theoretic Algorithms 32 String Matching 33 Machine-Learning Algorithms 34 NP-Completeness 35 Approximation Algorithms ... Introduction to Algorithms is a book on computer programming by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and ... The book has been widely used as the textbook for algorithms courses at many universities and is commonly cited as a reference ...
Incremental algorithms, or online algorithms, are algorithms in which only additions of elements are allowed, possibly starting ... "Dynamic graph algorithms". In CRC Handbook of Algorithms and Theory of Computation, Chapter 22. CRC Press, 1997. v t e ( ... Decremental algorithms are algorithms in which only deletions of elements are allowed, starting with the initialization of a ... If both additions and deletions are allowed, the algorithm is sometimes called fully dynamic. Static problem For a set of N ...
... determined from the worst case inputs to the algorithm. The term "analysis of algorithms" was coined by Donald Knuth. Algorithm ... Since algorithms are platform-independent (i.e. a given algorithm can be implemented in an arbitrary programming language on an ... Algorithm analysis is important in practice because the accidental or unintentional use of an inefficient algorithm can ... In computer science, the analysis of algorithms is the process of finding the computational complexity of algorithms-the amount ...
In computer algorithms, Block swap algorithms swap two regions of elements of an array. It is simple to swap two non- ... All three algorithms are linear time O(n), (see Time complexity). The reversal algorithm is the simplest to explain, using ... Three algorithms are known to accomplish this: Bentley's Juggling (also known as Dolphin Algorithm ), Gries-Mills, and Reversal ... The reversal algorithm uses three in-place rotations to accomplish an in-place block swap: Rotate region A Rotate region B ...
One such example of deadlock algorithm is Banker's algorithm. Distributed deadlocks can occur in distributed systems when ... algorithms, which track all cycles that cause deadlocks (including temporary deadlocks); and heuristics algorithms which don't ... algorithms, which track all cycles that cause deadlocks (including temporary deadlocks); and heuristics algorithms which don't ... A deadlock prevention algorithm organizes resource usage by each process to ensure that at least one process is always able to ...
However, most root-finding algorithms do not guarantee that they will find all the roots; in particular, if such an algorithm ... In mathematics and computing, a root-finding algorithm is an algorithm for finding zeros, also called "roots", of continuous ... since algebraic properties of polynomials are fundamental for the most efficient algorithms. The efficiency of an algorithm may ... In general, numerical algorithms are not guaranteed to find all the roots of a function, so failing to find a root does not ...
C4.5 algorithm, a descendant of ID3 decision tree algorithm, was developed by Ross Quinlan 1993 - Apriori algorithm developed ... It adds a soft-margin idea to the 1992 algorithm by Boser, Nguyon, Vapnik, and is the algorithm that people usually refer to ... 1956 - Kruskal's algorithm developed by Joseph Kruskal 1956 - Ford-Fulkerson algorithm developed and published by R. Ford Jr. ... 1977 - RSA encryption algorithm rediscovered by Ron Rivest, Adi Shamir, and Len Adleman 1977 - LZ77 algorithm developed by ...
... (ISSN 0937-5511) is a book series in mathematics, and particularly in combinatorics and the design ... 1) Geometric Algorithms and Combinatorial Optimization (Martin Grötschel, László Lovász, and Alexander Schrijver, 1988, vol. 2 ... 27) Sparsity: Graphs, Structures, and Algorithms (Jaroslav Nešetřil and Patrice Ossona de Mendez, 2012, vol. 28) Optimal ... and analysis of algorithms. It is published by Springer Science+Business Media, and was founded in 1987. As of 2018[update], ...
... and an algorithm for learning the fit parameters. Because the quantum algorithm is mainly based on the HHL algorithm, it ... The quantum algorithm provides a quadratic improvement over the best classical algorithm in the general case, and an ... Quantum optimization algorithms are quantum algorithms that are used to solve optimization problems. Mathematical optimization ... until a more effective classical algorithm was proposed. The relative speed-up of the quantum algorithm is an open research ...
A schema (PL: schemata) is a template in computer science used in the field of genetic algorithms that identifies a subset of ... In evolutionary computing such as genetic algorithms and genetic programming, propagation refers to the inheritance of ...
The Secure Hash Algorithms are a family of cryptographic hash functions published by the National Institute of Standards and ... All SHA-family algorithms, as FIPS-approved security functions, are subject to official validation by the CMVP (Cryptographic ... This was designed by the National Security Agency (NSA) to be part of the Digital Signature Algorithm. Cryptographic weaknesses ... A 160-bit hash function which resembles the earlier MD5 algorithm. ...
... are algorithms that can perform clustering without prior knowledge of data sets. In contrast ... Automated selection of k in a K-means clustering algorithm, one of the most used centroid-based clustering algorithms, is still ... Clustering algorithms artificially generated are compared to DBSCAN, a manual algorithm, in experimental results. Outlier " ... For instance, the Estimation of Distribution Algorithms guarantees the generation of valid algorithms by the directed acyclic ...
Shiloach, Yossi; Vishkin, Uzi (1982). "An O(n2 log n) parallel max-flow algorithm". Journal of Algorithms. 3 (2): 128-146. doi: ... In many respects, analysis of parallel algorithms is similar to the analysis of sequential algorithms, but is generally more ... In computer science, the analysis of parallel algorithms is the process of finding the computational complexity of algorithms ... An algorithm that exhibits linear speedup is said to be scalable. Efficiency is the speedup per processor, Sp / p. Parallelism ...
The European Symposium on Algorithms (ESA) is an international conference covering the field of algorithms. It has been held ... "Algorithms - ESA 2012 (Lecture Notes in Computer Science)" (PDF). 2012. Retrieved 2012-09-17.[dead link] "Test-of-Time Award - ... Since 2001, ESA is co-located with other algorithms conferences and workshops in a combined meeting called ALGO. This is the ... The intended scope was all research in algorithms, theoretical as well as applied, carried out in the fields of computer ...
For example, if one has a sorted list one will use a search algorithm optimal for sorted lists. The book was one of the most ... Algorithms + Data Structures = Programs is a 1976 book written by Niklaus Wirth covering some of the fundamental topics of ... Pdf at ETH Zurich) (archive.org link) Wirth, Niklaus (2004) [updated 2012]. Algorithms and Data Structures (PDF). Oberon ... Citations collected by the ACM ETH Zurich / N. Wirth / Books / Compilerbau: Algorithms + Data Structures = Programs (archive. ...
... (TALG) is a quarterly peer-reviewed scientific journal covering the field of algorithms. It was ... Algorithmica Algorithms (journal) Gabow, Hal. "Journal of Algorithms Resignation". Department of Computer Science, University ... The journal was created when the editorial board of the Journal of Algorithms resigned out of protest to the pricing policies ... "ACM Transactions on Algorithms". 2022 Journal Citation Reports. Web of Science (Science ed.). Thomson Reuters. 2022. "ACM ...
The algorithm for doing this involves finding an approximation to the diameter of the point set, and using a box oriented ... Finally, O'Rourke's algorithm is applied to find the exact optimum bounding box of this coreset. A Matlab implementation of the ... For the convex polygon, a linear time algorithm for the minimum-area enclosing rectangle is known. It is based on the ... In 1985, Joseph O'Rourke published a cubic-time algorithm to find the minimum-volume enclosing box of a 3-dimensional point set ...
Online algorithms with predictions have become a trending topic in the field of beyond worst-case analysis of algorithms. These ... Title:Online Algorithms with Uncertainty-Quantified Predictions. Authors:Bo Sun, Jerry Huang, Nicolas Christianson, Mohammad ... In general, the algorithm is assumed to be unaware of the predictions quality. However, recent developments in the machine ... View a PDF of the paper titled Online Algorithms with Uncertainty-Quantified Predictions, by Bo Sun and 4 other authors ...
Algorithms that do not represent all human populations. According to the results, people diagnosed with DS and MS presented the ... AI algorithms for diagnosing rare diseases do not include current human diversity Rare diseases, miscegenation and genetic ... "Moreover, we tested the accuracy of the diagnostic of an AI algorithm -known as Face2Gene- used in the clinical practice to ... AI algorithms for diagnosing rare diseases do not include current human diversity University of Barcelona ...
We use game theory to analyze meta-learning algorithms. The objective of meta-learning is to determine which algorithm to apply ...
Polynomial time algorithm to test isomorphism of arbitrary graphs ( with out any conditions ) is proposed. Some mistakes in the ... Efficient Algorithm for Graph Isomorphism Problem. EasyChair Preprint no. 798, version history. Rama Murthy Garimella ... 1. It is proved that the algorithm meets a lower bound on computational complexity for the graph isomorphism problems. New ... 1) A new lemma ( lemma 5 ) which is crucial for the goodness of algorithm is proved and included ...
Machine Learning algorithms coded from scratch. Contribute to andremonaco/cheapml development by creating an account on GitHub. ... Machine Learning algorithms coded from scratch Topics. data-science machine-learning random-forest machine-learning-algorithms ... There will be two blog posts about the code and the machine learning algorithms as whole. You can find the gradient boosting ... This repo contains machine learning algorithms coded from scratch. These implementations are not designed for high-performance ...
"This work will lead to algorithms that can avoid or at least detect that the learned policy, or actions, are likely to fail," ... Reinforcement algorithms are designed to perceive and interpret their environments, take actions and learn through trial and ... He expects the new, reliable algorithms will help bring data-driven decision-making to these new domains and plans to integrate ... One of the main reasons is that the existing algorithms are fragile and can fail catastrophically without a warning. ...
Algorithms for the Common Good, we are committed to ensuring that efforts to develop and use algorithms and artificial ... reframe[Tech] - Algorithms for the Common Good. In the project "reframe[Tech] - Algorithms for the Common Good", we are ... The use of algorithms has long since ceased to be science fiction and has become reality. We must therefore re-evaluate the ... How do algorithms influence our everyday life? We inform and discuss about their social consequences, chances and possible ...
Students alternative algorithms are often just as correct, efficient, and generalizable as the standard algorithms, and ... In elementary arithmetic, a standard algorithm or method is a specific method of computation which is conventionally taught for ... something that is usually lost in the memorization of standard algorithms). The development of sophisticated calculators has ...
Projects Cryptographic Algorithm Validation Program Cryptographic Algorithm Validation Program CAVP. Share to Facebook Share to ...
ITCS 2019) that is Õ(n^{1-1/2r}) for r ∈ {2,3}. Both our algorithms and the algorithms of Parter et al. use a combination of ... A simple and linear time randomized algorithm for computing sparse spanners in weighted graphs. Random Struct. Algorithms, 30(4 ... Improved Local Computation Algorithms for Constructing Spanners. Authors Rubi Arviv. , Lily Chung. , Reut Levi , Edward Pyne. * ... Algorithms and Techniques (APPROX/RANDOM 2023) Part of: Series: Leibniz International Proceedings in Informatics (LIPIcs) Part ...
... here are the 8 basic data structures and a short guide to algorithms. ... Characteristics of an algorithm We cant count each and every procedure (solution) as an algorithm. An algorithm must have ... What is an algorithm? An algorithm is a finite set of step-by-step (written in order) instructions to solve a specific problem ... Example of an algorithm Here is an example of an algorithm for subtracting two numbers and showing the result. ...
... Matt D md123 at nycap.rr.com Tue Dec 17 19:53:04 CET 2013 *Previous message: encryption algorithm ...
Google announces algorithm changes believed to target Demand, and Demand responds by Faith Merino on February 25, 2011 ... Google announces algorithm changes believed to target Demand, and Demand responds Fresh off the heels of its latest spat with a ... Generally speaking, Googles algorithm recognizes sites associated with ".edu" URLs to be of higher quality than others (since ... Matt Cutts announced in a blog post Thursday evening that Google has finally implemented some key changes to its algorithm to ...
... understanding TikToks algorithm is key to making your content take off. ... How does the TikTok algorithm work?. TikToks algorithm prioritizes engagement, video information, and the settings for your ... TikToks algorithm is a system that decides what content will be displayed on each users For You Page-its a recommendation ... The algorithm will display videos it determines are most likely to entice you on your FYP. As a living computation, TikToks ...
Basically, when an algorithm is given some initial state, it will use the ... The term algorithm is used in a variety of fields, including mathematics, computer programming, and linguistics. Its most well- ... Because computers use algorithms for every type of processing task they must complete, a computer algorithm can become very ... There is currently no formally accepted definition for what an algorithm can be, an algorithm must give a set of explicit ...
Shors factoring algorithm Shors algorithm is a quantum algorithm for factoring a number N in O((log N)3) time and O(log N) ... Explanation of the algorithm. The algorithm is composed of two parts. The first part of the algorithm turns the factoring ... Modifications to Shors Algorithm. There have been many modifications to Shors algorithm. For example, whereas, an order of ... Note: another way to explain Shors algorithm is by noting that it is just the quantum phase estimation algorithm in disguise. ...
dns-wg] Online DS and signing algorithm test. *Previous message (by thread): [dns-wg] New on RIPE Labs: Finding Open DNS ... and all signing algorithms. It does not do a full NSEC vs. NSEC3 test, as we assume that NSEC3 support likely also means NSEC ... NLnet Labs and SURFnet have set up an online test that determines which DS and signing algorithms the resolver(s) configured on ... You can find the test here: https://rootcanary.org/test.html The test includes 4 DS algorithms (SHA1, SHA256, SHA512, GOST) ...
nifi.sensitive.props.algorithm. in conjunction with updating nifi.sensitive.props.key. . Implementing a new command to set the ... nifi.sensitive.props.algorithm. and updates the flow configuration using the specified key. The NiFi encrypt-config.sh. toolkit ... to support updating the sensitive properties algorithm would streamline the upgrade process for deployments that currently use ... one of the older algorithms.. The set-sensitive-properties-key. command available in nifi.sh. supports changing the value of ...
From past 1 month keywords ranking is fluctuating so any algorithm google is updated? ... We are having Google algorithm updates and we need to keep a track of our content and backlinks to stay safe from any ranking ... From past 1 month keywords ranking is fluctuating so any algorithm google is updated? ...
Our algorithm is based on g Maximum Matching computations (total running time O(g m √{n + m/g}), where n=,V, and m=,E,) and a ... We give a (3/2)-approximation algorithm. Key to this problem is the following question: given a multigraph G=(V,E) of maximum ... they give a reduction from Edge Coloring showing that MTPS is NP-Hard and then implicitly give a 2-approximation algorithm. ...
Tag Archives: fancy algorithm. Column: search software Loose Wire - Organize Me: Give us some software that really makes the ... fancy algorithm, Index, Information retrieval, Information science, Microsoft, Microsoft Corporation, Outlook, search engine, ...
Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms ... HeteroPar 2022 : Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms Conference Series : Algorithms ... Algorithms, models and tools for grid, desktop grid, cloud, and green computing that include heterogeneous computing aspect ... New ideas, innovative algorithms, and specialized programming environments and tools are needed to efficiently use these new ...
Google has published a new algorithm. It can paraphrase your content to create brand new content. ... Googles new algorithm works by summarizing web content using an algorithm that "extracts" your content then tosses out the ... Afterwards, this algorithm then uses another kind of algorithm called an Abstractive Summary. Abstractive summaries are a form ... Is Googles Algorithm Summarizing Your Content?. This algorithm is about summarizing "multiple documents" and summarizing them ...
The QRISK2 algorithm includes all the major risk factors of the FRS-ATP-III score, plus the following [14] :. * Self-reported ... Risk Algorithms. The guidelines that cover the screening of patients for elevated serum lipid levels, and the treatment of ... Commonly used risk algorithms developed with European population cohorts include the following:. * Systematic Coronary Risk ... The most commonly used risk algorithms developed with United States population cohorts include the following:. * Framingham ...
It is proved that the new algorithm can terminate at an ,svg style=vertical-align:-0.1638pt;width:7.0999999px; id=M1 height ... Moreover, no line search is needed in this algorithm, and the global convergence can be proved under mild conditions. Numerical ... Hence, we propose an accelerated proximal gradient algorithm for singly linearly constrained quadratic programs with box ... which show that the new algorithm is efficient. ... the existed proximal gradient algorithms had been used to solve ...
Algorithm terminates and prints out max flow. if (parent[sink] == null) break; // If sink WAS reached, we will push more flow ... Java Implementation of Edmonds-Karp Algorithm * * By: Pedro Contipelli * Input Format: (Sample Input) N , E , (N total nodes , ... Algorithm Implementation/Graphs/Maximum flow/Edmonds-Karp. From Wikibooks, open books for an open world ... is the direct transcription in MATLAB language of the pseudocode shown in the Wikipedia article of the Edmonds-Karp algorithm. ...
Moreover, the task of porting algorithms to these heterogeneous machines typically requires that the algorithm be partitioned ... Compiling Algorithms for Heterogeneous Systems. Steven Bell, Stanford University. Jing Pu, Google. James Hegarty, Oculus. Mark ... From there, Chapter 3 provides a brief introduction to image processing algorithms and hardware design patterns for ... enabling rapid design cycles and quick porting of algorithms. The final section describes how the DSL approach also simplifies ...
Bitcoin Forum > Alternate cryptocurrencies > Announcements (Altcoins) > Obyte: Totally new consensus algorithm + private ... Bitcoin Forum > Alternate cryptocurrencies > Announcements (Altcoins) > Obyte: Totally new consensus algorithm + private ... Topic: Obyte: Totally new consensus algorithm + private untraceable payments (Read 1233945 times) ... Totally new consensus algorithm + private untraceable payments ... Re: BYTEBALL: Totally new consensus algorithm + private ...
An Efficient Denoising Algorithm for Global Illumination. We propose a hybrid ray-tracing/rasterization strategy for real-time ... An Efficient Denoising Algorithm for Global Illumination ...
  • write high-level computational programs and quality-assured numerical software, · implement and test complex numerical algorithms using well-established software libraries, · to carry out a programming project in a group including identification of, and division in, partial problems and personal responsibility for the solution of a partial problem, · describe a computational project through an oral presentation of his/her own code. (lu.se)
  • assess the performance of complex numerical algorithms, · argue for the importance of developing programs in a modular and flexible way, · critically analyze other students' solutions and presentations and evaluate alternative solutions in relation to their own solutions. (lu.se)
  • Examples of complex numerical algorithms from different fields within numerical analysis. (lu.se)
  • Finn argues that the algorithm deploys concepts from the idealized space of computation in a messy reality, with unpredictable and sometimes fascinating results. (mit.edu)
  • In elementary arithmetic, a standard algorithm or method is a specific method of computation which is conventionally taught for solving particular mathematical problems. (wikipedia.org)
  • In this talk, I will present verification methods for randomized algorithms based on a simple guiding principle: align classical verification methods with the techniques algorithm designers use to prove correctness. (cornell.edu)
  • I will also show how verification methods designed for certifying correctness of compiler transformations can also capture a powerful proof technique from probability theory called 'proof by coupling', enabling clean verification of properties including privacy of probabilistic queries, incentive compatibility of randomized mechanisms, and stability of learning algorithms. (cornell.edu)
  • Including populations of Amerindian, African, Asian and European origins in the AI-generated algorithms is decisive for improving the diagnostic methods of rare diseases, as stated in an article published in Nature's journal Scientific Reports . (eurekalert.org)
  • We present the results of an evaluation study comparing traditional, manual surveillance methods to alternative methods with available clinical electronic data and computer algorithms to identify bloodstream infections. (cdc.gov)
  • In particular, we consider predictions augmented with uncertainty quantification describing the likelihood of the ground truth falling in a certain range, designing online algorithms with these probabilistic predictions for two classic online problems: ski rental and online search. (arxiv.org)
  • In each case, we demonstrate that non-trivial modifications to algorithm design are needed to fully leverage the probabilistic predictions. (arxiv.org)
  • Probabilistic programs and randomized algorithms play an important role in many leading areas of computer science. (cornell.edu)
  • His research focuses on formal verification of probabilistic programs, including algorithms from differential privacy, protocols from cryptography, and mechanisms from game theory. (cornell.edu)
  • The CART algorithm has been extensively applied in predictive studies, however, researchers argue that CART produces variable selection bias. (bvsalud.org)
  • Considering this problem, this article compares the CART algorithm to an unbiased algorithm (CTREE), in relation to their predictive power. (bvsalud.org)
  • When reviewing the history of the Regression Tree Method and its algorithms, Loh (2014) argues that some well-studied and largely applied Regression Tree algorithms, e.g. (bvsalud.org)
  • Part I covers elementary data structures, sorting, and searching algorithms. (coursera.org)
  • For example, if one has a sorted list one will use a search algorithm optimal for sorted lists. (wikipedia.org)
  • This textbook thoroughly outlines combinatorial algorithms for generation, enumeration, and search. (routledge.com)
  • You can Google for how to create an algorithm for films in javascript [ ^ ] or similar search. (codeproject.com)
  • To receive continuing education (CE) for WD4520-092123 - Clinician Outreach and Communication Activity (COCA) Calls/Webinars - Algorithms for Diagnosing the Endemic Mycoses Blastomycosis, Coccidioidomycosis, and Histoplasmosis , please visit CDC TRAIN and search for the course in the Course Catalog using WD4520-092123 . (cdc.gov)
  • VIENNA - An algorithm, the Steatosis-Associated Fibrosis Estimator (SAFE), was developed to detect clinically significant fibrosis in patients with nonalcoholic fatty liver disease (NAFLD). (medscape.com)
  • Part II focuses on graph- and string-processing algorithms. (coursera.org)
  • Algorithms + Data Structures = Programs [1] is a 1976 book written by Niklaus Wirth covering some of the fundamental topics of system engineering , computer programming , particularly that algorithms and data structures are inherently related. (wikipedia.org)
  • N. Wirth, Algorithms and Data Structures (1985 edition, updated for Oberon in August 2004. (wikipedia.org)
  • This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. (coursera.org)
  • Not only can you use these packages immediately, they also incubate new algorithms and data structures for eventual inclusion in the Swift Standard Library. (apple.com)
  • We'll show you how you can integrate these packages into your projects and select the right algorithms and data structures to make your code clearer and faster. (apple.com)
  • He expects the new, reliable algorithms will help bring data-driven decision-making to these new domains and plans to integrate the research with educational activities to provide graduate and undergraduate students with training opportunities and new study materials, including a textbook. (unh.edu)
  • My animated approach to Data Structures & Algorithms will help you quickly grasp complex concepts and retain more information, making your coding journey easier and more efficient. (udemy.com)
  • With over 100 hand-crafted HD videos, you'll receive a thorough understanding of Data Structures & Algorithms that will leave you feeling confident and prepared. (udemy.com)
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  • Over 100 hand-crafted animated HD videos to illustrate the Data Structures & Algorithms. (udemy.com)
  • therefore, the algorithms can be applied to clinical data from the previous day. (cdc.gov)
  • This result suggests that for large data sets, called big data, the CART algorithm might give better results than the CTREE algorithm. (bvsalud.org)
  • We create and improve algorithms and software for correcting and analysing spectroscopic data. (lu.se)
  • People who are interested in digging deeper into the content may wish to obtain the textbook Algorithms, Fourth Edition (upon which the course is based) or visit the website algs4.cs.princeton.edu for a wealth of additional material. (coursera.org)
  • This work serves as an exceptional textbook for a modern course in combinatorial algorithms, providing a unified and focused collection of recent topics of interest in the area. (routledge.com)
  • Moreover, we tested the accuracy of the diagnostic of an AI algorithm -known as Face2Gene - used in the clinical practice to identify these diseases through the analysis of facial morphometric traits. (eurekalert.org)
  • Participants will also be introduced to new clinical diagnostic algorithms to address these challenges and improve the timely diagnosis of blastomycosis, coccidioidomycosis, and histoplasmosis. (cdc.gov)
  • Today I will take on the difficult task of comparing the American Association of Clinical Endocrinologists (AACE) algorithms for the treatment of type 2 diabetes [ 1 ] with the position statement of the American Diabetes Association/European Association for the Study of Diabetes (ADA/EASD) for the management of hyperglycemia in type 2 diabetes. (medscape.com)
  • MFT is benchmarked against the back-propagation learning algorithm (BP) on two different feature recognition problems: two-dimensional mirror symmetry and multidimensional statistical pattern classification. (lu.se)
  • Statistical antecedents of CART algorithm are of historical importance since they trace back to 1960s, when the Automatic Interaction Detection (AID) algorithm was created (Morgan & Sonquist, 1963). (bvsalud.org)
  • Cite this: AACE Algorithm Offers New Guidance on Managing T2DM - Medscape - May 31, 2013. (medscape.com)
  • We illustrate our basic approach to developing and analyzing algorithms by considering the dynamic connectivity problem. (coursera.org)
  • Students' alternative algorithms are often just as correct, efficient, and generalizable as the standard algorithms, and maintain emphasis on the meaning of the quantities involved, especially as relates to place values (something that is usually lost in the memorization of standard algorithms). (wikipedia.org)
  • The diagnostic accuracy of the deep learning automated algorithm used in the study was very high in the case of DS and very low (less than 10%) in MS and NF1. (eurekalert.org)
  • Compared to an European sample, the study reveals that, despite the diagnostic accuracy for Down syndrome was 100% in both populations, the variation in the average facial similarities between people diagnosed with DS and the automated algorithm model was significantly larger in the Colombian sample. (eurekalert.org)
  • The Classification and Regression Trees (CART) algorithm is a traditional, popular, and well-developed approach of the Regression Tree Method (Loh, 2014). (bvsalud.org)
  • Ethics of Algorithms - How do we bring AI from principles to practice? (bertelsmann-stiftung.de)
  • explain the basic principles of computational algorithms, · describe the typical requirements that are set when testing computational software in relation to software in other application areas, · describe in detail a number of important computational problems and ways to tackle them. (lu.se)
  • The mean field theory (MFT) learning algorithm is elaborated and explored with respect to a variety of tasks. (lu.se)
  • The theory concentrates mainly on spatial concepts and algorithms (both vector and raster). (lu.se)
  • War and Algorithm' look at the increasing power of algorithms in these emerging forms of warfare from the perspectives of critical theory, philosophy, legal studies, and visual studies. (lu.se)
  • These algorithms incorporate predictions about the future to obtain performance guarantees that are of high quality when the predictions are good, while still maintaining bounded worst-case guarantees when predictions are arbitrarily poor. (arxiv.org)
  • We present survey of Belief Network algorithms and propose a domain characterisation system to be used as a basis for algorithm comparison and for predicting algorithm performance. (aaai.org)
  • The basis of our approach for analyzing the performance of algorithms is the scientific method. (coursera.org)
  • In this paper, we analyze and compare the performance of machine learning based algorithms like K-Nearest Neighbour, Random Forest, Logistic Regression and Decision Tree. (easychair.org)
  • So the chain of algorithms is clearer and more concise than the raw loop, but how does the performance compare? (apple.com)
  • Using this paradigm, we make some first steps towards an indefinite summation algorithm applicable to summands that rationally depend on the summation index and a P-recursive sequence and its shifts. (arxiv.org)
  • Online algorithms with predictions have become a trending topic in the field of beyond worst-case analysis of algorithms. (arxiv.org)
  • Various algorithms are programmed to automatise the analysis of information gathered about you. (lu.se)
  • 16. 'Additional Site of Disease' (Q16a) is not included in the algorithm used to determine when the RVCT record is complete except when 'Additional Site of Disease' (Q16a) is equal to Other (80). (cdc.gov)
  • It takes a little investment to learn the vocabulary, but once you do, it can be striking to discover just how many algorithms are hiding in plain sight and how much you can improve the quality of your code by adopting them. (apple.com)
  • 2/4 complex computational algorithms and modern programming language. (lu.se)
  • A new infrastructure for generic algorithms that builds on top of the new iterator concepts. (boost.org)
  • This document describes the algorithms used to determine when a TIMS Surveillance record is complete. (cdc.gov)
  • Swift Algorithms is an open-source package of sequence and collection algorithms that augments the Swift standard library. (apple.com)
  • We can express this more concisely by chaining together algorithms from the standard library -- reversed, compactMap, and prefix -- to take no more than the first six. (apple.com)
  • This work will lead to algorithms that can avoid or at least detect that the learned policy, or actions, are likely to fail," says Petrik. (unh.edu)
  • and computer algorithms and manual CVC determination. (cdc.gov)
  • The κ value was 0.37 for infection control review, 0.48 for positive blood culture plus manual CVC determination, 0.49 for computer algorithm, and 0.73 for computer algorithm plus manual CVC determination. (cdc.gov)
  • In this book, Ed Finn considers how the algorithm-in practical terms, "a method for solving a problem"-has its roots not only in mathematical logic but also in cybernetics, philosophy, and magical thinking. (mit.edu)
  • Both algorithms were applied to the 2011 National Exam of High School Education, which includes many categorical predictors with a large number of categories, which could produce a variable selection bias. (bvsalud.org)
  • I will focus first on that position statement and then I will discuss the AACE algorithms. (medscape.com)
  • Furthermore, it is difficult to compare them while using the terms "algorithm," "position statement," and "guidelines" correctly. (medscape.com)
  • That Google algorithm update from last Wednesday seems to still be going on. (seroundtable.com)
  • I think the current Algorithm Update of May 17 - May 18 is still in progress. (seroundtable.com)
  • I think we are dealing with a Panda related Algorithm Update. (seroundtable.com)
  • One of the most powerful features of Swift is the rich taxonomy of algorithms that come built in. (apple.com)