Intelligent optimization algorithms have advantages in dealing with complex nonlinear problems accompanied by good flexibility and adaptability. In this paper, the FCBF (Fast Correlation-Based Feature selection) method is used to filter irrelevant and redundant features in order to improve the quality of cancer classification. Then, we perform classification based on SVM (Support Vector Machine) optimized by PSO (Particle Swarm Optimization) combined with ABC (Artificial Bee Colony) approaches, which is represented as PA-SVM. The proposed PA-SVM method is applied to nine cancer datasets, including five datasets of outcome prediction and a protein dataset of ovarian cancer. By comparison with other classification methods, the results demonstrate the effectiveness and the robustness of the proposed PA-SVM method in handling various types of data for cancer classification.
TY - JOUR. T1 - Predictive vaccinology. T2 - 6th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2005. AU - Bozic, Ivana. AU - Zhang, Guang Lan. AU - Brusic, Vladimir. PY - 2005/1/1. Y1 - 2005/1/1. N2 - Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 supertype molecules with excellent accuracy, even for molecules where no binding data are currently available.. AB - Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial ...
This paper presents a novel application of particle swarm optimization (PSO) in combination with another computational intelligence (CI) technique, namely, proximal support vector machine (PSVM) for machinery fault detection. Both real-valued and binary PSO algorithms have been considered along with.... Full description. ...
Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support
Tumor classification and segmentation from brain computed tomography image data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue among different patients and in many cases, similarity between tumor and normal tissue. This paper deals with an efficient segmentation algorithm for extracting the brain tumors in computed tomography images using Support Vector Machine classifier. The objective of this work is to compare the dominant grey level run length feature extraction method with wavelet based texture feature extraction method and SGLDM method. A dominant gray level run length texture feature set is derived from the region of interest (ROI) of the image to be selected. The optimal texture features are selected using Genetic Algorithm. The selected optimal run length texture features are fed to the Support Vector Machine classifier (SVM) to classify and segment the tumor from brain
Abstract: In this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods.The improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between ...
A novel quantitative analysis method of multi-component mixture gas concentration based on support vector machine (SVM) and spectroscopy is proposed. Through transformation of the kernel function, the seriously overlapped and nonlinear spectrum data are transformed in high-dimensional space, but the high-dimensional data can be processed in the original space. Some factors, such as kernel function, range of the wavelength, and penalty coefficient, are discussed. This method is applied to the quantitative analysis of natural gas components concentration, and the component concentration maximal deviation is 2.28%.. © 2005 Chinese Optics Letters. PDF Article ...
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Classification of broad area features in satellite imagery is one of the most important applications of remote sensing. It is often difficult and time-consuming to develop classifiers by hand, so many researchers have turned to techniques from the fields of statistics and machine learning to automatically generate classifiers. Common techniques include maximum likelihood classifiers, neural networks and genetic algorithms. We present a new system called Afreet, which uses a recently developed machine learning paradigm called Support Vector Machines (SVMs). In contrast to other techniques, SVMs offer a solid mathematical foundation that provides a probabilistic guarantee on how well the classifier will generalize to unseen data. In addition the SVM training algorithm is guaranteed to converge to the globally optimal SVM classifier, can learn highly non-linear discrimination functions, copes extremely well with high-dimensional feature spaces (such as hype spectral data), and scales well to large ...
Elucidation of the interaction of proteins with different molecules is of significance in the understanding of cellular processes. Computational methods have been developed for the prediction of protein-protein interactions. But insufficient attention has been paid to the prediction of protein-RNA interactions, which play central roles in regulating gene expression and certain RNA-mediated enzymatic processes. This work explored the use of a machine learning method, support vector machines (SVM), for the prediction of RNA-binding proteins directly from their primary sequence. Based on the knowledge of known RNA-binding and non-RNA-binding proteins, an SVM system was trained to recognize RNA-binding proteins. A total of 4011 RNA-binding and 9781 non-RNA-binding proteins was used to train and test the SVM classification system, and an independent set of 447 RNA-binding and 4881 non-RNA-binding proteins was used to evaluate the classification accuracy. Testing results using this independent ...
The Bayes rule is the optimal classification rule if the underlying distribution of the data is known. In practice we do not know the underlying distribution, and need to ``learn classification rules from the data. One way to derive classification rules in practice is to implement the Bayes rule approximately by estimating an appropriate classification function. Traditional statistical methods use estimated log odds ratio as the classification function. Support vector machines (SVMs) are one type of large margin classifier, and the relationship between SVMs and the Bayes rule was not clear. In this paper, it is shown that SVMs implement the Bayes rule approximately by targeting at some interesting classification functions. This helps understand the success of SVMs in many classification studies, and makes it easier to compare SVMs and traditional statistical methods.
In state-of-the-art phrase-based statistical machine translation systems, modelling phrase reorderings is an important need to enhance naturalness of the translated outputs, particularly when the grammatical structures of the language pairs differ significantly. Posing phrase movements as a classification problem, we exploit recent developments in solving large-scale multiclass support vector machines. Using dual coordinate descent methods for learning, we provide a mechanism to shrink the amount of training data required for each iteration. Hence, we produce significant computational saving while preserving the accuracy of the models. Our approach is a couple of times faster than maximum entropy approach and more memory-efficient (50% reduction). Experiments were carried out on an Arabic-English corpus with more than a quarter of a billion words. We achieve BLEU score improvements on top of a strong baseline system with sparse reordering features. ...
Stable feature selection based on the ensemble L1-norm support vector machine for biomarker discovery. . Biblioteca virtual para leer y descargar libros, documentos, trabajos y tesis universitarias en PDF. Material universiario, documentación y tareas realizadas por universitarios en nuestra biblioteca. Para descargar gratis y para leer online.
RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM) approach was used to quantitatively model RNA interference activities. Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams) and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5 most base of the guide strand is the most informative. The capacity of
To solve the multi-class fault diagnosis tasks, decision tree support vector machine (DTSVM), which combines SVM and decision tree using the concept of dic
Predictive modeling can be a valuable tool for systems designers, allowing them to capture and reuse knowledge from a set of observed data related to their system. An important challenge associated with predictive modeling is that of describing the domain over which model predictions are valid. This is necessary to avoid extrapolating beyond the original data, particularly when designers use predictive models in concert with optimizers or other computational routines that search a models input space automatically. The general problem of domain description is complicated by the characteristics of observational data sets, which can contain small numbers of samples, can have nonlinear associations among the variables, can be non-convex, and can occur in largely disjoint clusters. Support Vector Machine (SVM) techniques, developed originally in the machine learning community, offer a solution to this problem. This paper is a description of a kernel-based SVM approach that yields a formal ...
The current method to match mass spectra from tandem mass spectrometry (MS) to a peptide sequence requires searching a large database of all possible peptides encoded by an organism. However, only a subset of these possible peptides is consistently and repeatedly identified by MS (proteotypic peptides). Matching spectra to this smaller, proteotypic peptide search space increases computational efficiency and improves accuracy of the peptide identification, hence increasing the confidence that a protein has been accurately identified. Currently, it is labor-intensive to build a proteotypic peptide database of experimentally observed peptides! thus computationally deriving such a database is desirable. Webb-Robertson et al. trained a statistical learning algorithm called a support vector machine (SVM) from Yersinia pestis data that computationally classifies a peptide as proteotypic or not proteotypic. Preliminary tests by these authors showed that this SVM accurately predicted proteotypic peptides for two
While the genomes of hundreds of organisms have been sequenced and good approaches exist for finding protein encoding genes, an important remaining challenge is predicting the functions of the large fraction of genes for which there is no annotation. Large gene expression datasets from microarray experiments already exist and many of these can be used to help assign potential functions to these genes. We have applied Support Vector Machines (SVM), a sigmoid fitting function and a stratified cross‐validation approach to analyze a large microarray experiment dataset from Drosophila melanogaster in order to predict possible functions for previously un‐annotated genes. A total of approximately 5043 different genes, or about one‐third of the predicted genes in the D. melanogaster genome, are represented in the dataset and 1854 (or 37%) of these genes are un‐annotated. 39 Gene Ontology Biological Process (GO‐BP) categories were found with precision value equal or larger than 0.75, when recall was
Variable selection with supervised classification is currently an important tool for discriminating biological samples. In this paper, 15 supervised classification algorithms based on a support vector machine (SVM) were applied to discriminate Cryptococcus neoformans and Cryptococcus gattii fungal species us
A Support Vector Machine (SVM) was trained to distinguish bursts from suppression in burst-suppression EEG, using Ave features inherent in the electro-encephalogram (EEG) as input. The study was based on data from six full term infants who had suffered from perinatal asphyxia, and the machine was trained with reference classifications made by an experienced electroencephalographer. The results show that the method may be useful, but that differences between patients in the data set makes optimization of the system difficult.. ...
View Notes - D18 from MBAHRM 565 at IIT Kanpur. The material covered in the first five chapters has given us the foundation on which to introduce Support Vector Machines, the learning approach
Prediction of the severity of obstructive sleep apnea by anthropometric features via support vector machine. . Biblioteca virtual para leer y descargar libros, documentos, trabajos y tesis universitarias en PDF. Material universiario, documentación y tareas realizadas por universitarios en nuestra biblioteca. Para descargar gratis y para leer online.
The current application concerns a new approach for disease recognition of vine leaves based on Local Binary Patterns (LBPs). The LBP approach was applied on color digital pictures with a natural complex background that contained infected leaves. The pictures were captured with a smartphone camera from vine plants. A 32-bin histogram was calculated by the LBP characteristic features that resulted from a Hue plane. Moreover, four One Class Support Vector Machines (OCSVMs) were trained with a training set of 8 pictures from each disease including healthy, Powdery Mildew and Black Rot and Downy Mildew. The trained OCSVMs were tested with 100 infected vine leaf pictures corresponding to each disease which were capable of generalizing correctly, when presented with vine leave which was infected by the same disease. The recognition percentage reached 97 %, 95 % and 93 % for each disease respectively while healthy plants were recognized with an accuracy rate of 100 %.
Alpha-helical transmembrane (TM) proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are also prime drug targets, and it has been estimated that more than half of all drugs currently on the market target membrane proteins. However, due to the experimental difficulties involved in obtaining high quality crystals, this class of protein is severely under-represented in structural databases. In the absence of structural data, sequence-based prediction methods allow TM protein topology to be investigated. We present a support vector machine-based (SVM) TM protein topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of 131 sequences with known crystal structures. The method achieves topology prediction accuracy of 89%, while signal peptides and re-entrant helices
The recent increase of smart meters in the residential sector has lead to large available datasets. The electricity consumption of individual households can be accessed in close to real time, and allows both the demand and supply side to extract valuable information for efficient energy management. Predicting electricity consumption should help utilities improve planning generation and demand side management, however this is not a trivial task as consumption at the individual household level is highly irregular.. In this thesis the problem of improving load forecasting is ad-dressed using two machine learning methods, Support Vector Machines for regression (SVR) and Random Forest. For a customer base consisting of 187 households in Austin, Texas, pre-dictions are made on three spatial scales: (1) individual house-hold level (2) aggregate level (3) clusters of similar households according to their daily consumption profile. Results indicate that using Random Forest with K = 32 clusters yields ...
BioMed Research International is a peer-reviewed, Open Access journal that publishes original research articles, review articles, and clinical studies covering a wide range of subjects in life sciences and medicine. The journal is divided into 55 subject areas.
Motivated by the unmet clinical need for accurate markers for pancreatic cancer, this study was conducted with the aim to identify and pre-validate disease specific serum protein signatures, using an in-house, state-of-the-art, recombinant antibody microarray platform. Directly biotinylated sera from 148 individuals with pancreatic cancer (PC), chronic pancreatitis (CP), autoimmune pancreatits (AIP), and normal, healthy controls (N), were profiled on arrays consisting of 121 antibodies mainly targeting immunoregulatory proteins. Bound analytes were visualized using confocal fluorescence scanning. Using a support vector machine classifier with leave-one-out cross validation, the screening revealed serum protein portraits distinguishing PC from N (Area Under ROC-Curve (AUC) 0.95), CP (AUC 0.86), and AIP (AUC 0.99), respectively. In order to further condense the candidate biomarker profiles, we applied an iterative backward elimination strategy, with which two distinct protein signatures were ...
Background A thorough analysis of continuous adventitious sounds (CAS) can provide distinct and complementary information about bronchodilator response (BDR), beyond that provided by spirometry. Nevertheless, previous approaches to CAS analysis were limited by certain methodology issues. The aim of this study is to propose a new integrated approach to CAS analysis that contributes to improving the assessment of BDR in clinical practice for asthma patients. Methods Respiratory sounds and flow were recorded in 25 subjects, including 7 asthma patients with positive BDR (BDR+), assessed by spirometry, 13 asthma patients with negative BDR (BDR-), and 5 controls. A total of 5149 acoustic components were characterized using the Hilbert spectrum, and used to train and validate a support vector machine classifier, which distinguished acoustic components corresponding to CAS from those corresponding to other sounds. Once the method was validated, BDR was assessed in all participants by CAS analysis, and ...
Regression analysis is one of methods widely used in prediction problems. Although there are many methods used for parameter estimation in regression analysis, ordinary least squares (OLS) technique is the most commonly used one among them. However, this technique is highly sensitive to outlier observation. Therefore, in literature, robust techniques are suggested when data set includes outlier observation. Besides, in prediction a problem, using the techniques that reduce the effectiveness of outlier and using the median as a target function rather than an error mean will be more successful in modeling these kinds of data. In this study, a new parameter estimation method using the median of absolute rate obtained by division of the difference between observation values and predicted values by the observation value and based on particle swarm optimization was proposed. The performance of the proposed method was evaluated with a simulation study by comparing it with OLS and some other robust ...
article{10:73,author={Christian Rieger and Barbara Zwicknagl}, Title={Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/rieger09a/rieger09a.pdf ...
Quantitative characterization of carotid atherosclerosis and classification into symptomatic or asymptomatic type is crucial in both diagnosis and treatment planning. This paper describes a computer-aided diagnosis (CAD) system which analyzes ultrasound images and classifies them into symptomatic and asymptomatic based on the textural features. The proposed CAD system consists of three modules. The first module is preprocessing, which conditions the images for the subsequent feature extraction. The feature extraction stage uses image texture analysis to calculate Standard deviation, Entropy, Symmetry, and Run Percentage. Finally, classification is performed using AdaBoost and Support Vector Machine for automated decision making. For Adaboost, we compared the performance of five distinct configurations (Least Squares, Maximum- Likelihood, Normal Density Discriminant Function, Pocket, and Stumps) of this algorithm. For Support Vector Machine, we compared the performance using five different ...
A specific XML (MODS) intermediate renderer. The XML intermediate format is conform to the Library of Congresss Metadata Object Description Schema (MODS). This is a very flexible standard that should prove quite useful as the number of tools that directly interact with it increase ...
The parameters of the maximum-margin hyperplane are derived by solving the optimization. There exist several specialized algorithms for quickly solving the QP problem that arises from SVMs, mostly relying on heuristics for breaking the problem down into smaller, more-manageable chunks. Another approach is to use an interior point method that uses Newton-like iterations to find a solution of the Karush-Kuhn-Tucker conditions of the primal and dual problems.[34] Instead of solving a sequence of broken down problems, this approach directly solves the problem altogether. To avoid solving a linear system involving the large kernel matrix, a low rank approximation to the matrix is often used in the kernel trick. Another common method is Platts sequential minimal optimization (SMO) algorithm, which breaks the problem down into 2-dimensional sub-problems that are solved analytically, eliminating the need for a numerical optimization algorithm and matrix storage. This algorithm is conceptually simple, ...
Background & Objective: Studies have shown that despite the numerous research carried out regarding infertility treatment, there is still a long way to treat this disease satisfactorily. Spending a lot of time and money on infertility treatments proves the necessity of designing a model which could predict the result of treatment methods with ...
Recognition of plants has become an active area of research as most of the plant species are at the risk of extinction. This paper uses an efficient machin
Related Articles The predictive performance of short-linear motif features in the prediction of calmodulin-binding proteins. BMC Bioinformatics. 2018 Nov 20;19(Suppl 14):410 Authors: Li Y, Maleki ...
In this paper, a novel algorithm is introduced for the analysis of long-memory patterns hidden in electromagnetic (EM) readings prior to earthquakes. The algorithm builds..
Background The prediction of the prokaryotic promoter strength based on its sequence is of great importance not only in the fundamental research of life sciences but also in the applied aspect of...
SVM and Kernel Methods Matlab Toolbox. Key Features: SVM Classification using linear and quadratic penalization of misclassified examples ( penalization coefficients can be different for each examples); SVM Classification with Nearest Point Algorithm; Multiclass SVM : one against all, one against one and M-SVM; Large Scale SVM Classification/Regression; SVM epsilon and nu regression; One-Class SVM; Regularisation Networks; SVM bounds (Span estimate, radius/margin); Wavelet Kernel; SVM Based Feature Selection; Kernel PCA; Kernel Discriminant Analysis; SVM Based Feature selection; SVM AUC Optimization (Ranking SVM, ROC SVM) and RankBoost; Kernel Basis Pursuit and Least Angle Regression (LARS) Algorithm; Wavelet Kernel Regression with backfitting; Interface with a version of libsvm.
Using the PBMC gene expression data, we identified biological pathways that showed the greatest differences in expression between women who developed FBC and women who had a family history of breast cancer but who did not develop a tumor (Fig 1A). We obtained gene lists for 932 biological pathways from: (i) KEGG (Kanehisa et al, 2006) (accessed on June 16, 2011), (ii) the Molecular Signatures Database (v3.0) (Subramanian et al, 2005), and (iii) two research articles (Taube et al, 2010; Byers et al, 2013). For a given pathway, we made predictions in two successive steps: (i) we identified the most discriminatory genes from that pathway using the Support Vector Machines‐Recursive Feature Elimination (SVM‐RFE) algorithm (Guyon et al, 2002) and then (ii) used the SVM classification algorithm (Vapnik, 1998) to derive a probability that each patient had developed FBC. For the Utah individuals with a family history of breast cancer, we derived a probability for each sample in a ten‐fold ...
This paper presents a computer-aided screening system (DREAM) that analyzes fundus images with varying illumination and fields of view, and generates a severity grade for diabetic retinopathy (DR) using machine learning. Classifiers such as the Gaussian Mixture model (GMM), k-nearest neighbor (kNN), support vector machine (SVM), and AdaBoost are analyzed for classifying retinopathy lesions from nonlesions. GMM and kNN classifiers are found to be the best classifiers for bright and red lesion classification, respectively. A main contribution of this paper is the reduction in the number of features used for lesion classification by feature ranking using Adaboost where 30 top features are selected out of 78. A novel two-step hierarchical classification approach is proposed where the nonlesions or false positives are rejected in the first step. In the second step, the bright lesions are classified as hard exudates and cotton wool spots, and the red lesions are classified as hemorrhages and ...
In this article, a hybrid approach comprising of two conventional machine learning algorithms is proposed to carry out attribute selection. Genetic algorithms (GAs) and Support Vector Machines (SVMs) are integrated effectively based on a wrapper approach. Specifically, the GA component searches for the best attribute set by applying the principles of an evolutionary process. The SVM then classifies the patterns in the reduced datasets, corresponding to the attribute subsets represented by the GA chromosomes. Simulation results demonstrate that the GA-SVM hybrid produces good classification accuracy and a higher level of consistency that is comparable to other established algorithms. In addition, improvements are made to the hybrid by using a correlation measure between attributes as a fitness measure to replace the weaker members in the population with newly formed chromosomes ...
Functional brain changes induced by chemotherapy are still not well characterized. We used a novel approach with a multivariate technique to analyze brain resting state [18 F]FDG-PET in patients with lymphoma, to explore differences on cerebral metabolic glucose rate between chemotherapy-treated and non-treated patients. PET/CT scan was performed on 28 patients, with 14 treated with systemic chemotherapy. We used a support vector machine (SVM) classification, extracting the mean metabolism from the metabolic patterns, or networks, that discriminate the two groups. We calculated the correct classifications of the two groups using the mean metabolic values extracted by the networks. The SVM classification analysis gave clear-cut patterns that discriminate the two groups. The first, hypometabolic network in chemotherapy patients, included mostly prefrontal cortex and cerebellar areas (central executive network, CEN, and salience network, SN); the second, which is equal between groups, included mostly
This is the 4th installment of my Practical Machine Learning with R and Python series. In this part I discuss classification with Support Vector Machines (SVMs), using both a Linear and a Radial basis kernel, and Decision Trees. Further, a closer look is taken at some of the metrics associated with binary classification, namely accuracy…
In the clinical laboratory, large amounts of laboratory data are accumulated, which may provide clues to aid in the prediction of various disease states. However, most work has focused on finding a single marker for diagnosis, which oftentimes perform poorly. The reason for this is that combinations of multiple markers are what need to be used for prediction, complicating matters. In the bioinformatics field, support vector machines (SVMs) for classification and feature extraction have gained much popularity due to their ability to handle multi-dimensional data and to pinpoint (combinations of) specific features that are key to the classification problem at hand. Using our preoperative clinical laboratory data based on 254 surgically operated and histologically confirmed pancreatic cancer patients, we attempted to classify the extent of cancer growth by (1) first finding the most pertinent markers, or features, for classification, (2) training the SVM classifier using the selected features, and ...
A patient state is detected with at least one classification boundary generated by a supervised machine learning technique, such as a support vector machine. The patient state can be, for example, a patient posture state. In some examples, the patient state detection is used to at least one of control the delivery of therapy to a patient, to generate a patient notification, to initiate data recording, or to evaluate a patient condition. In addition, an evaluation metric can be determined based on a feature vector, which is determined based on characteristics of a patient parameter signal, and the classification boundary. Example evaluation metrics can be based on a distance between at least one feature vector and the classification boundary and/or a trajectory of a plurality of feature vectors relative to the classification boundary over time.
Phos3D is a web server for the prediction of phosphorylation sites (P-sites) in proteins. The approach is based on Support Vector Machines trained on sequence profiles enhanced by information from the spatial context of experimentally identified P-sites. In addition to serine, threonine, and tyrosine P-sites, Phos3D is capable to predict kinase-specific phosphorylations by the serine kinases PKA, PKC, MAPK, and CKII, as well as by the tyrosine kinase SRC. The quality of predictions is greatly dependent on the quality of submitted protein structures. Erroneous or incomplete protein structures may lead to inaccurate predictions.. ...
Cys2His2 zinc finger (ZF) proteins represent the largest class of eukaryotic transcription factors. We present an approach for predicting ZF binding based on support vector machines (SVMs).
Last modified: The scientific discipline of Machine Learning is concerned with algorithmic paradigms and techniques that allow machines to learn from experience. Given the vast quantities of data that are collected in the modern world, Machine Learning has become increasingly important in order to utilize the knowledge inherent in this data. In this graduate course, we will examine, in depth, a variety of techniques used in machine learning and data mining and also examines issues associated with their application. Topics include algorithms for supervised learning including decision trees, artificial neural networks, probabilistic methods, boosting, and support vector machines; and unsupervised learning including clustering and principal components analysis. Also covers computational learning theory and other methods for analyzing and measuring the performance of learning algorithms. The course is largely self-contained. This document, and all documents on this website, may be modified from time ...
Computational and Mathematical Methods in Medicine is a peer-reviewed, Open Access journal that publishes research and review articles focused on the application of mathematics to problems arising from the biomedical sciences. Areas of interest include gene therapy, cell kinetics, pharmacokinetics, chemotherapy, oncology, developmental biology, wound healing, physiology, heart modelling, cardiovascular and lung dynamics, neurobiology, computational neuroscience, biomechanics, biomedical statistics, image analysis, epidemiology, immunology, time series analysis, extracellular matrix properties and signalling, and tissue engineering.