Markov Chains: 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.Monte Carlo Method: 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)Bayes Theorem: 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.Algorithms: A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.Models, Genetic: 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.Models, Statistical: 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.Computer Simulation: Computer-based representation of physical systems and phenomena such as chemical processes.Likelihood Functions: 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.Stochastic Processes: Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables.Phylogeny: The relationships of groups of organisms as reflected by their genetic makeup.Software: Sequential operating programs and data which instruct the functioning of a digital computer.Genealogy and HeraldryProbability: The study of chance processes or the relative frequency characterizing a chance process.Models, Biological: 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.Evolution, Molecular: The process of cumulative change at the level of DNA; RNA; and PROTEINS, over successive generations.Computational Biology: 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.Chromosome Mapping: Any method used for determining the location of and relative distances between genes on a chromosome.Sequence Analysis, DNA: A multistage process that includes cloning, physical mapping, subcloning, determination of the DNA SEQUENCE, and information analysis.Sequence Alignment: 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.Data Interpretation, Statistical: Application of statistical procedures to analyze specific observed or assumed facts from a particular study.Models, Theoretical: 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.Pattern Recognition, Automated: In INFORMATION RETRIEVAL, machine-sensing or identification of visible patterns (shapes, forms, and configurations). (Harrod's Librarians' Glossary, 7th ed)Biometry: The use of statistical and mathematical methods to analyze biological observations and phenomena.Biostatistics: The application of STATISTICS to biological systems and organisms involving the retrieval or collection, analysis, reduction, and interpretation of qualitative and quantitative data.Genetics, Population: The discipline studying genetic composition of populations and effects of factors such as GENETIC SELECTION, population size, MUTATION, migration, and GENETIC DRIFT on the frequencies of various GENOTYPES and PHENOTYPES using a variety of GENETIC TECHNIQUES.Polymerase Chain Reaction: In vitro method for producing large amounts of specific DNA or RNA fragments of defined length and sequence from small amounts of short oligonucleotide flanking sequences (primers). The essential steps include thermal denaturation of the double-stranded target molecules, annealing of the primers to their complementary sequences, and extension of the annealed primers by enzymatic synthesis with DNA polymerase. The reaction is efficient, specific, and extremely sensitive. Uses for the reaction include disease diagnosis, detection of difficult-to-isolate pathogens, mutation analysis, genetic testing, DNA sequencing, and analyzing evolutionary relationships.Quantitative Trait, Heritable: A characteristic showing quantitative inheritance such as SKIN PIGMENTATION in humans. (From A Dictionary of Genetics, 4th ed)Quantitative Trait Loci: Genetic loci associated with a QUANTITATIVE TRAIT.Molecular Sequence Data: 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.Genetic Markers: A phenotypically recognizable genetic trait which can be used to identify a genetic locus, a linkage group, or a recombination event.Quality-Adjusted Life Years: A measurement index derived from a modification of standard life-table procedures and designed to take account of the quality as well as the duration of survival. This index can be used in assessing the outcome of health care procedures or services. (BIOETHICS Thesaurus, 1994)Cost-Benefit Analysis: A method of comparing the cost of a program with its expected benefits in dollars (or other currency). The benefit-to-cost ratio is a measure of total return expected per unit of money spent. This analysis generally excludes consideration of factors that are not measured ultimately in economic terms. Cost effectiveness compares alternative ways to achieve a specific set of results.Population Dynamics: The pattern of any process, or the interrelationship of phenomena, which affects growth or change within a population.Sequence Analysis, Protein: 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.Base Sequence: The sequence of PURINES and PYRIMIDINES in nucleic acids and polynucleotides. It is also called nucleotide sequence.Genetic Linkage: The co-inheritance of two or more non-allelic GENES due to their being located more or less closely on the same CHROMOSOME.Classification: The systematic arrangement of entities in any field into categories classes based on common characteristics such as properties, morphology, subject matter, etc.Population Density: Number of individuals in a population relative to space.Reproducibility of Results: 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.Multifactorial Inheritance: A phenotypic outcome (physical characteristic or disease predisposition) that is determined by more than one gene. Polygenic refers to those determined by many genes, while oligogenic refers to those determined by a few genes.Probability Learning: Usually refers to the use of mathematical models in the prediction of learning to perform tasks based on the theory of probability applied to responses; it may also refer to the frequency of occurrence of the responses observed in the particular study.Artificial Intelligence: 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.Cluster Analysis: 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.Normal Distribution: 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.Pedigree: The record of descent or ancestry, particularly of a particular condition or trait, indicating individual family members, their relationships, and their status with respect to the trait or condition.Genetic Variation: Genotypic differences observed among individuals in a population.Genotype: The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.Alleles: Variant forms of the same gene, occupying the same locus on homologous CHROMOSOMES, and governing the variants in production of the same gene product.

*  Glorified Markov Chain

... by Ross Burton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 ...

*  markov 1 | Markov Chain | Vector Space

Such a chain is called a Markov chain and the matrix M is called a transition matrix. The state vectors can be of one of two ... Markov Processes. 1. Introduction Before we give the definition of a Markov process, we will look at an example: Example 1: ... Definition 2. A Markov process is a stochastic process with the following properties: (a.) The number of possible outcomes or ... P = 0.2 Find the transition matrix M and steady-state vector xs for this Markov process.2 −0.6552 and xs = 0. then dim(N (M − ...

*  Biodiversity | CSPs: Tree species stem breaking probability deviation CSPs

We used Bayesian inference and Gibbs Sampling via Markov Chain Monte Carlo (JAGS: Plummer, 2003) with uninformative priors to ...

*  A model for a stochastic growth process with interactions - Durham University

The Markov Chain has sequential stochastic growth dynamics in which the probability of adding a new particle to a vertex is ... For a directed graph containing N vertices, I introduce an N-dimensional discrete time Markov chain with positive integer ...

*  Java markov chain generate text Jobs, Employment | Freelancer

Search for jobs related to Java markov chain generate text or hire on the world's largest freelancing marketplace with 12m+ ... markov chain java, markov chain text generator, markov chain text, markov chain text generation, markov chain text generator ... markov chain example demo text, markov chain text generation java, java markov chain text, java markov chain, markov chain text ... content generate markov chain, text markov chain php, markov chain generator text, java markov chain generator, markov chain ...

*  Jointly Optimal Quantization, Estimation, and Control of Hidden Markov Chains

... and control of a hidden Markov chain. We first formulate the joint quantization and estimation problem, where vector ...

*  Markov Chains and Dependability Theory : Gerardo Rubino : 9781139989534

Markov Chains and Dependability Theory by Gerardo Rubino, 9781139989534, available at Book Depository with free delivery ... 1. Introduction; 2. Discrete time Markov chains; 3. Continuous time Markov chains; 4. State aggregation of Markov chains; 5. ... Topics covered include Markovian state lumping, analysis of sojourns on subset of states of Markov chains, analysis of most ... The authors first present both discrete and continuous time Markov chains before focusing on dependability measures, which ...

*  Hashcat Per Position Markov Chains

... more specifically their implementation of Markov chains. The Markov model is a mathematical system that has had numerous uses ... more specifically their implementation of Markov chains.. The Markov model is a mathematical system that has had numerous uses ... A Markov chain, in this case, is a statistical analysis of a list of words and is already being used by password cracking tools ... The good news for Hashcat users is that it also has a '--markov-classic' option. Where I've seen the best results for the per- ...

*  Modeling chutes and ladders as a Markov chain | Isaac Slavitt

In an absorbing Markov Chain, there exists at least one state such that we never leave once we get to it. This is called the ... Modeling chutes and ladders as a Markov chain. 03 Jan 2014. Every month, the magazine ORMS Today features an Operations ... It's pretty well known that chutes and ladders can be modeled as an absorbing Markov Chain. Given the rules of the game and the ... If you enjoyed reading about this problem, you can read more about Markov Chains here. Here's another PuzzlOR which is similar ...

*  Lotsa 'Splainin' 2 Do: Wednesday Math, Vol. 14: Markov chains

It is very difficult to study Markov chain topic. Not many good reference textbooks to study Markov chain.. I use Markov Chains ... The math done here is known as a Markov chain. It is used for problems like this dealing with the flow of stuff, whether people ... The most recent class I had on Markov chains was taught from the notes of the professor, so I can't give a good reference. ... Do you have any other good Markov Chains related textbooks recommend?. Regards,. Andy ...

*  Efficient state classification of finite state Markov chains - IEEE Conference Publication

This paper presents an efficient method for state classification of finite state Markov chains using BDD-based symbolic ...

*  Markov Chain Archives - Basic Statistics and Data Analysis

Example: Markov Chain. A Markov chain $X$ on $S=\{0,1\}$ is determined by the initial distribution given by $p_0=P(X_0=0), \; p ... A Markov chain, named after Andrey Markov is a mathematical system that experience transitions from one state to another, ... Formally a Markov chain is a sequence of random variables $X_1,X_2,\cdots,$ with the Markov property that, given the present ... This specific kind of memorylessness is called the Markov property. Markov chains have many applications as statistical models ...

*  Reflections on the Numerical Solution of Markov Chains - IEEE Conference Publication

... the first software system to generate and then compute the stationary distribution of Markov chains ...

*  A New Improved Parsimonious Multivariate Markov Chain Model : Figure 1

Figure 1: The prediction errors of the new improved parsimonious multivariate Markov chain model with our new convergence ...

*  Granger-causality in Markov Switching Models

... we investigate the causality and interdependence between financial and economic cycles using a bivariate Markov switching model ... propose a new parametrisation of transition probabilities that allows us to characterize and test Granger-causality in Markov ... Keywords: Granger Causality; Markov Chains; Switching Models; Other versions of this item:. * Monica Billio & Silvio Di Sanzo, ... "A Markov-switching structural vector autoregressive model of boom and bust in the Australian labour market," Empirical ...

*  Markov Chain CFAR Detection for Polarimetric Data Using Data Fusion - IEEE Journals & Magazine

This paper proposes a new Markov-chain-based constant false alarm rate (CFAR) detector for polarimetric data using low-level ...

*  Re: Markov Chain Program

in reply to Markov Chain Program. There are some good examples of Markov chain code floating around on here that break up the ... I need some help for Markov algorithm for followign question Markov chain algorithm that will allow you to write a program to ... Re: Re: Re: Markov Chain Program by Anonymous Monk on Nov 14, 2002 at 05:28 UTC ... Re: Re: Markov Chain Program by Anonymous Monk on Aug 29, 2001 at 19:34 UTC ...

*  Petascale : Probable Cause: Modeling with Markov Chains

Markov Chains have numerous applications in biology from ecology to bioinformatics. This module will explore some of these ... Probable Cause: Modeling with Markov Chains. By Angela B. Shiflet, George W. Shiflet and Whitney Sanders Wofford College, ... Produce a serial and parallel implementation of Markov Chains in MatLab. * Produce a serial and parallel implementation of ...

*  A comparative evaluation of stochastic-based inference methods for Gaussian process models | SpringerLink

Flegal, J. M., Haran, M., & Jones, G. L. (2007). Markov chain Monte Carlo: can we trust the third significant figure? ... Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo methods (Technical Report CRG-TR-93-1). Dept. of ... Bayesian inference Gaussian processes Markov chain Monte Carlo Hierarchical models Latent variable models ... a number of inference strategies based on Markov chain Monte Carlo methods are presented and rigorously assessed. In particular ...

*  Gene Finding in Viral Genomes

FGENESV algorithm is based on pattern recognition of different types of signals and Markov chain models of coding regions. ... FGENESV algorithm is based on pattern recognition of different types of signals and Markov chain models of coding regions. ... fgenesV0 - Generic parameters Markov chain-based viral gene prediction [Help] [Example]. fgenesV - Trained Pattern/Markov chain ...

*  Fast Bayesian whole-brain fMRI analysis with spatial 3D priors

We introduce a fast and practical Markov chain Monte Carlo (MCMC) scheme for exact inference in the same model, both slice-wise ... We introduce a fast and practical Markov chain Monte Carlo (MCMC) scheme for exact inference in the same model, both slice-wise ... We introduce a fast and practical Markov chain Monte Carlo (MCMC) scheme for exact inference in the same model, both slice-wise ... We introduce a fast and practical Markov chain Monte Carlo (MCMC) scheme for exact inference in the same model, both slice-wise ...

*  Model-checking algorithms for continuous-time Markov chains - IEEE Journals & Magazine

Continuous-time Markov chains (CTMCs) have been widely used to determine system performance and dependability characteristics. ...

*  Landmark Kernel tICA for Conformational Dynamics

The first key insight was when Swope and Pitera 4 applied the theory of Markov chains to protein dynamics. As researchers ... Markov State Models (MSMs) 2-4 and time-structure based independent component analysis (tICA) 5-7 have been introduced to ... 3. Prinz, J.-H. et al. Markov models of molecular kinetics: Generation and validation. J. Chem. Phys. J Chem Phys 134, 174105 ( ... 2. Pande, V. S., Beauchamp, K. & Bowman, G. R. Everything you wanted to know about Markov State Models but were afraid to ask. ...

*  Broader Perspective: Genomic polymorphisms trigger phantom limb pain and synesthesia

side chains (1) * signal transmission (1) * signaling (1) * signaling networks (1) * signaling pathways (1) ... markov (1) * mars (1) * marsh mcluhan (1) * masdar (1) * mash-up (1) ...

*  2013-2014

This side chain may have several degrees of unsaturation, with more carbon-carbon double bonds equating with a stronger ... and the Hidden Markov Model has predicted that the P. sojae genome contains approximately 400 RXLR-dEER effectors, forming a ... The model represents chromosomes as self-avoiding polymer chains confined within the nucleus; parameters of the model are taken ... a catechol with a 15 or 17 carbon side chain, that is primarily responsible for the allergic reaction in people. ...

Vladimir Andreevich Markov: Vladimir Andreevich Markov (; May 8, 1871 – January 18, 1897) was a Russian mathematician, known for proving the Markov brothers' inequality with his older brother Andrey Markov. He died of tuberculosis at the age of 25.Monte Carlo methods for option pricing: In mathematical finance, a Monte Carlo option model uses Monte Carlo methods Although the term 'Monte Carlo method' was coined by Stanislaw Ulam in the 1940s, some trace such methods to the 18th century French naturalist Buffon, and a question he asked about the results of dropping a needle randomly on a striped floor or table. See Buffon's needle.Hyperparameter: In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the underlying system under analysis.Clonal Selection Algorithm: In artificial immune systems, Clonal selection algorithms are a class of algorithms inspired by the clonal selection theory of acquired immunity that explains how B and T lymphocytes improve their response to antigens over time called affinity maturation. These algorithms focus on the Darwinian attributes of the theory where selection is inspired by the affinity of antigen-antibody interactions, reproduction is inspired by cell division, and variation is inspired by somatic hypermutation.Inverse probability weighting: Inverse probability weighting is a statistical technique for calculating statistics standardized to a population different from that in which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application.Interval boundary element method: Interval boundary element method is classical boundary element method with the interval parameters.
Decoding methods: In coding theory, decoding is the process of translating received messages into codewords of a given code. There have been many common methods of mapping messages to codewords.Doob decomposition theorem: In the theory of stochastic processes in discrete time, a part of the mathematical theory of probability, the Doob decomposition theorem gives a unique decomposition of every adapted and integrable stochastic process as the sum of a martingale and a predictable process (or "drift") starting at zero. The theorem was proved by and is named for Joseph L.Branching order of bacterial phyla (Gupta, 2001): There are several models of the Branching order of bacterial phyla, one of these was proposed in 2001 by Gupta based on conserved indels or protein, termed "protein signatures", an alternative approach to molecular phylogeny. Some problematic exceptions and conflicts are present to these conserved indels, however, they are in agreement with several groupings of classes and phyla.Mac OS X Server 1.0Atomic heraldry: Atomic heraldry is heraldry characterised by the appearance of charges including the atom or showing the motion of parts of the atom; more loosely, it may describe heraldry in which atoms or the component parts thereof are represented through a combination of other charges. Obviously, this is a late development in heraldry.Negative probability: The probability of the outcome of an experiment is never negative, but quasiprobability distributions can be defined that allow a negative probability for some events. These distributions may apply to unobservable events or conditional probabilities.Matrix model: == Mathematics and physics ==Molecular evolution: Molecular evolution is a change in the sequence composition of cellular molecules such as DNA, RNA, and proteins across generations. The field of molecular evolution uses principles of evolutionary biology and population genetics to explain patterns in these changes.PSI Protein Classifier: PSI Protein Classifier is a program generalizing the results of both successive and independent iterations of the PSI-BLAST program. PSI Protein Classifier determines belonging of the found by PSI-BLAST proteins to the known families.Chromosome regionsDNA sequencer: A DNA sequencer is a scientific instrument used to automate the DNA sequencing process. Given a sample of DNA, a DNA sequencer is used to determine the order of the four bases: G (guanine), C (cytosine), A (adenine) and T (thymine).CS-BLASTVon Neumann regular ring: In mathematics, a von Neumann regular ring is a ring R such that for every a in R there exists an x in R such that . To avoid the possible confusion with the regular rings and regular local rings of commutative algebra (which are unrelated notions), von Neumann regular rings are also called absolutely flat rings, because these rings are characterized by the fact that every left module is flat.A-scan ultrasound biometry: A-scan ultrasound biometry, commonly referred to as an A-scan, is routine type of diagnostic test used in ophthalmology. The A-scan provides data on the length of the eye, which is a major determinant in common sight disorders.Biostatistics (journal): Biostatistics is a peer-reviewed scientific journal covering biostatistics, that is, statistics for biological and medical research. The journals that had cited Biostatistics the most by 2008Journal Citation Reports 2008, Science Edition were Biometrics, Journal of the American Statistical Association, Biometrika, Statistics in Medicine, and Journal of the Royal Statistical Society, Series B.Panmixia: Panmixia (or panmixis) means random mating.King C and Stanfield W.Thermal cyclerColes PhillipsDisease burden: Disease burden is the impact of a health problem as measured by financial cost, mortality, morbidity, or other indicators. It is often quantified in terms of quality-adjusted life years (QALYs) or disability-adjusted life years (DALYs), both of which quantify the number of years lost due to disease (YLDs).Incremental cost-effectiveness ratio: The incremental cost-effectiveness ratio (ICER) is a statistic used in cost-effectiveness analysis to summarise the cost-effectiveness of a health care intervention. It is defined by the difference in cost between two possible interventions, divided by the difference in their effect.Matrix population models: Population models are used in population ecology to model the dynamics of wildlife or human populations. Matrix population models are a specific type of population model that uses matrix algebra.Protein subcellular localization prediction: Protein subcellular localization prediction (or just protein localization prediction) involves the computational prediction of where a protein resides in a cell.Symmetry element: A symmetry element is a point of reference about which symmetry operations can take place. In particular, symmetry elements can be centers of inversion, axes of rotation and mirror planes.Genetic linkage: Genetic linkage is the tendency of alleles that are located close together on a chromosome to be inherited together during the meiosis phase of sexual reproduction. Genes whose loci are nearer to each other are less likely to be separated onto different chromatids during chromosomal crossover, and are therefore said to be genetically linked.Glossary of scientific names: A glossary of the meaning of scientific names of living things, viruses and prions .Threshold host density: Threshold host density (NT), in the context of wildlife disease ecology, refers to the concentration of a population of a particular organism as it relates to disease. Specifically, the threshold host density (NT) of a species refers to the minimum concentration of individuals necessary to sustain a given disease within a population.Generalizability theory: Generalizability theory, or G Theory, is a statistical framework for conceptualizing, investigating, and designing reliable observations. It is used to determine the reliability (i.Mexican International Conference on Artificial Intelligence: MICAI (short for Mexican International Conference on Artificial Intelligence) is the name of an annual conference covering all areas of Artificial Intelligence (AI), held in Mexico. The first MICAI conference was held in 2000.STO-nG basis sets: STO-nG basis sets are minimal basis sets, where n primitive Gaussian orbitals are fitted to a single Slater-type orbital (STO). n originally took the values 2 - 6.Pedigree chart: A pedigree chart is a diagram that shows the occurrence and appearance or phenotypes of a particular gene or organism and its ancestors from one generation to the next,pedigree chart Genealogy Glossary - About.com, a part of The New York Times Company.Genetic variation: right|thumbInfinite alleles model: The infinite alleles model is a mathematical model for calculating genetic mutations. The Japanese geneticist Motoo Kimura and American geneticist James F.

(1/3175) Genome-wide bioinformatic and molecular analysis of introns in Saccharomyces cerevisiae.

Introns have typically been discovered in an ad hoc fashion: introns are found as a gene is characterized for other reasons. As complete eukaryotic genome sequences become available, better methods for predicting RNA processing signals in raw sequence will be necessary in order to discover genes and predict their expression. Here we present a catalog of 228 yeast introns, arrived at through a combination of bioinformatic and molecular analysis. Introns annotated in the Saccharomyces Genome Database (SGD) were evaluated, questionable introns were removed after failing a test for splicing in vivo, and known introns absent from the SGD annotation were added. A novel branchpoint sequence, AAUUAAC, was identified within an annotated intron that lacks a six-of-seven match to the highly conserved branchpoint consensus UACUAAC. Analysis of the database corroborates many conclusions about pre-mRNA substrate requirements for splicing derived from experimental studies, but indicates that splicing in yeast may not be as rigidly determined by splice-site conservation as had previously been thought. Using this database and a molecular technique that directly displays the lariat intron products of spliced transcripts (intron display), we suggest that the current set of 228 introns is still not complete, and that additional intron-containing genes remain to be discovered in yeast. The database can be accessed at http://www.cse.ucsc.edu/research/compbi o/yeast_introns.html.  (+info)

(2/3175) Economic consequences of the progression of rheumatoid arthritis in Sweden.

OBJECTIVE: To develop a simulation model for analysis of the cost-effectiveness of treatments that affect the progression of rheumatoid arthritis (RA). METHODS: The Markov model was developed on the basis of a Swedish cohort of 116 patients with early RA who were followed up for 5 years. The majority of patients had American College of Rheumatology (ACR) functional class II disease, and Markov states indicating disease severity were defined based on Health Assessment Questionnaire (HAQ) scores. Costs were calculated from data on resource utilization and patients' work capacity. Utilities (preference weights for health states) were assessed using the EQ-5D (EuroQol) questionnaire. Hypothetical treatment interventions were simulated to illustrate the model. RESULTS: The cohort distribution among the 6 Markov states clearly showed the progression of the disease over 5 years of followup. Costs increased with increasing severity of the Markov states, and total costs over 5 years were higher for patients who were in more severe Markov states at diagnosis. Utilities correlated well with the Markov states, and the EQ-5D was able to discriminate between patients with different HAQ scores within ACR functional class II. CONCLUSION: The Markov model was able to assess disease progression and costs in RA. The model can therefore be a useful tool in calculating the cost-effectiveness of different interventions aimed at changing the progression of the disease.  (+info)

(3/3175) Multipoint oligogenic analysis of age-at-onset data with applications to Alzheimer disease pedigrees.

It is usually difficult to localize genes that cause diseases with late ages at onset. These diseases frequently exhibit complex modes of inheritance, and only recent generations are available to be genotyped and phenotyped. In this situation, multipoint analysis using traditional exact linkage analysis methods, with many markers and full pedigree information, is a computationally intractable problem. Fortunately, Monte Carlo Markov chain sampling provides a tool to address this issue. By treating age at onset as a right-censored quantitative trait, we expand the methods used by Heath (1997) and illustrate them using an Alzheimer disease (AD) data set. This approach estimates the number, sizes, allele frequencies, and positions of quantitative trait loci (QTLs). In this simultaneous multipoint linkage and segregation analysis method, the QTLs are assumed to be diallelic and to interact additively. In the AD data set, we were able to localize correctly, quickly, and accurately two known genes, despite the existence of substantial genetic heterogeneity, thus demonstrating the great promise of these methods for the dissection of late-onset oligogenic diseases.  (+info)

(4/3175) Machine learning approaches for the prediction of signal peptides and other protein sorting signals.

Prediction of protein sorting signals from the sequence of amino acids has great importance in the field of proteomics today. Recently, the growth of protein databases, combined with machine learning approaches, such as neural networks and hidden Markov models, have made it possible to achieve a level of reliability where practical use in, for example automatic database annotation is feasible. In this review, we concentrate on the present status and future perspectives of SignalP, our neural network-based method for prediction of the most well-known sorting signal: the secretory signal peptide. We discuss the problems associated with the use of SignalP on genomic sequences, showing that signal peptide prediction will improve further if integrated with predictions of start codons and transmembrane helices. As a step towards this goal, a hidden Markov model version of SignalP has been developed, making it possible to discriminate between cleaved signal peptides and uncleaved signal anchors. Furthermore, we show how SignalP can be used to characterize putative signal peptides from an archaeon, Methanococcus jannaschii. Finally, we briefly review a few methods for predicting other protein sorting signals and discuss the future of protein sorting prediction in general.  (+info)

(5/3175) Genome-wide linkage analyses of systolic blood pressure using highly discordant siblings.

BACKGROUND: Elevated blood pressure is a risk factor for cardiovascular, cerebrovascular, and renal diseases. Complex mechanisms of blood pressure regulation pose a challenge to identifying genetic factors that influence interindividual blood pressure variation in the population at large. METHODS AND RESULTS: We performed a genome-wide linkage analysis of systolic blood pressure in humans using an efficient, highly discordant, full-sibling design. We identified 4 regions of the human genome that show statistical significant linkage to genes that influence interindividual systolic blood pressure variation (2p22.1 to 2p21, 5q33.3 to 5q34, 6q23.1 to 6q24.1, and 15q25.1 to 15q26.1). These regions contain a number of candidate genes that are involved in physiological mechanisms of blood pressure regulation. CONCLUSIONS: These results provide both novel information about genome regions in humans that influence interindividual blood pressure variation and a basis for identifying the contributing genes. Identification of the functional mutations in these genes may uncover novel mechanisms for blood pressure regulation and suggest new therapies and prevention strategies.  (+info)

(6/3175) FORESST: fold recognition from secondary structure predictions of proteins.

MOTIVATION: A method for recognizing the three-dimensional fold from the protein amino acid sequence based on a combination of hidden Markov models (HMMs) and secondary structure prediction was recently developed for proteins in the Mainly-Alpha structural class. Here, this methodology is extended to Mainly-Beta and Alpha-Beta class proteins. Compared to other fold recognition methods based on HMMs, this approach is novel in that only secondary structure information is used. Each HMM is trained from known secondary structure sequences of proteins having a similar fold. Secondary structure prediction is performed for the amino acid sequence of a query protein. The predicted fold of a query protein is the fold described by the model fitting the predicted sequence the best. RESULTS: After model cross-validation, the success rate on 44 test proteins covering the three structural classes was found to be 59%. On seven fold predictions performed prior to the publication of experimental structure, the success rate was 71%. In conclusion, this approach manages to capture important information about the fold of a protein embedded in the length and arrangement of the predicted helices, strands and coils along the polypeptide chain. When a more extensive library of HMMs representing the universe of known structural families is available (work in progress), the program will allow rapid screening of genomic databases and sequence annotation when fold similarity is not detectable from the amino acid sequence. AVAILABILITY: FORESST web server at http://absalpha.dcrt.nih.gov:8008/ for the library of HMMs of structural families used in this paper. FORESST web server at http://www.tigr.org/ for a more extensive library of HMMs (work in progress). CONTACT: valedf@tigr.org; munson@helix.nih.gov; garnier@helix.nih.gov  (+info)

(7/3175) Age estimates of two common mutations causing factor XI deficiency: recent genetic drift is not necessary for elevated disease incidence among Ashkenazi Jews.

The type II and type III mutations at the FXI locus, which cause coagulation factor XI deficiency, have high frequencies in Jewish populations. The type III mutation is largely restricted to Ashkenazi Jews, but the type II mutation is observed at high frequency in both Ashkenazi and Iraqi Jews, suggesting the possibility that the mutation appeared before the separation of these communities. Here we report estimates of the ages of the type II and type III mutations, based on the observed distribution of allelic variants at a flanking microsatellite marker (D4S171). The results are consistent with a recent origin for the type III mutation but suggest that the type II mutation appeared >120 generations ago. This finding demonstrates that the high frequency of the type II mutation among Jews is independent of the demographic upheavals among Ashkenazi Jews in the 16th and 17th centuries.  (+info)

(8/3175) Does over-the-counter nicotine replacement therapy improve smokers' life expectancy?

OBJECTIVE: To determine the public health benefits of making nicotine replacement therapy available without prescription, in terms of number of quitters and life expectancy. DESIGN: A decision-analytic model was developed to compare the policy of over-the-counter (OTC) availability of nicotine replacement therapy with that of prescription ([symbol: see text]) availability for the adult smoking population in the United States. MAIN OUTCOME MEASURES: Long-term (six-month) quit rates, life expectancy, and smoking attributable mortality (SAM) rates. RESULTS: OTC availability of nicotine replacement therapy would result in 91,151 additional successful quitters over a six-month period, and a cumulative total of approximately 1.7 million additional quitters over 25 years. All-cause SAM would decrease by 348 deaths per year and 2940 deaths per year at six months and five years, respectively. Relative to [symbol: see text] nicotine replacement therapy availability, OTC availability would result in an average gain in life expectancy across the entire adult smoking population of 0.196 years per smoker. In sensitivity analyses, the benefits of OTC availability were evident across a wide range of changes in baseline parameters. CONCLUSIONS: Compared with [symbol: see text] availability of nicotine replacement therapy, OTC availability would result in more successful quitters, fewer smoking-attributable deaths, and increased life expectancy for current smokers.  (+info)



Monte Carlo

  • Recent work incorporating geometric ideas in Markov chain Monte Carlo is reviewed in order to highlight these advances and their possible application in a range of domains beyond statistics. (mdpi.com)
  • A full exposition of Markov chains and their use in Monte Carlo simulation for statistical inference and molecular dynamics is provided, with particular emphasis on methods based on Langevin diffusions. (mdpi.com)
  • After this, geometric concepts in Markov chain Monte Carlo are introduced. (mdpi.com)
  • The first is to introduce geometric concepts that have recently been employed in Monte Carlo methods based on Markov chains [ 1 ] to a wider audience. (mdpi.com)
  • Two Markov chain Monte Carlo methods were introduced, the manifold Metropolis-adjusted Langevin algorithm and Riemannian manifold Hamiltonian Monte Carlo. (mdpi.com)
  • We take an expository approach, providing a review of some necessary preliminaries from Markov chain Monte Carlo, diffusion processes and Riemannian geometry. (mdpi.com)
  • This paper details Particle Markov chain Monte Carlo techniques for analysis of unobserved component time series models using several economic data sets. (repec.org)
  • It investigates the efficient application of Markov Chain Monte Carlo methods to matrix inversion, sparse approximate inverse preconditioning and solving of systems of linear algebraic equations. (bl.uk)

processes

  • Markov chains have many applications as statistical models of real world processes. (itfeature.com)
  • The book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. (ebooks.com)

probability

  • The Markov chain has the characteristic property that the probability that $X_n=j$ depends only on the immediate previous state of the system. (itfeature.com)
  • If the chain is currently in state $s_i$ then it moves to state $s_j$ at the next step with probability denoted by $p_{ij}$ (transition probability) and this probability does not depend upon which states the chain was in before the current state. (itfeature.com)