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Babelomics
Serves for the functional analysis of gene expression and genomic data. Babelomics offers the possibility to explore the effects of alteration in gene expression levels or changes in genes sequences within a functional context. It provides user-friendly access to a full range of methods that cover: (1) primary data analysis; (2) a variety of tests for different experimental designs; and (3) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context.
Decision Forest
Outperforms decision tree in both training and validation. Decision forest is an ensemble method developed by combining the predictions from multiple independent decision tree models to reach a better prediction. This method yields much high prediction accuracy in the high confidence regions compared to decision tree. Decision forest generally gives higher positive predictivity than other method, and even higher positive predictivity within definable high confidence regions.
mRMR / minimum Redundancy Maximum Relevance
Provides a theoretical analysis of the minimal-redundancy-maximal-relevance condition. mRMR is a framework allowing users to minimize redundancy, and it uses a series of intuitive measures of relevance and redundancy to select promising features for both continuous and discrete data sets. The incremental selection scheme of this method avoids the difficult multivariate density estimation in maximizing dependency. It can also be combined with other feature selectors.
PROMISE / Projection onto the most interesting statistical evidence
Identifies genes with expression values that exhibit a specific pattern of correlations with the endpoint variables defined by prior biological knowledge. PROMISE is a statistical procedure that designed to determine whether a variable exhibits a specific pattern of correlations with a set of other variables. The software performs an integrated analysis of microarray gene expression data with multiple endpoint variables. It offers an alternative approach for evaluating genomically-based pleiotropic phenotypes.
Tspair / Top Scoring Pairs
A package which can be used to quickly identify and assess TSP (Top Scoring Pairs) classifiers for gene expression data. Tspair can rapidly calculate the TSP for typical gene expression datasets, with tens of thousands of genes. The TSP can be calculated both in R or with an external C function, which allows both for rapid calculation and flexible development of the tspair package. It includes functions for calculating the statistical significance of a TSP by permutation test, and is fully compatible with Bioconductor expression sets.
PROPS / PRObabilistic Pathway Score
Allows individualized pathway-based classification. PROPS creates individualized features that reflect pathway activity using Gaussian Bayesian networks. It can calculate the log likelihood of each patient’s data, which can be interpreted as a measure of pathway perturbation and dysregulation. This tool takes into account pathway topology, rather than treating pathways as gene sets. It contributes biological insight into differentiating Crohn’s disease (CD) and ulcerative colitis (UC).
GeneSrF
Implements a validated method for gene selection including bootstrap estimates of classification error rate. GeneSrF is a web-based tool for applied biomedical researchers, specially for exploratory work with microarray data. Because of the underlying technology used (combination of parallelization with web-based application) they are also of methodological interest to bioinformaticians and biostatisticians. GeneSrF is also available as an R package: varSelRF, and is part of the suite ASTERIAS.
varSelRF
Implements a validated method for gene selection including bootstrap estimates of classification error rate. varSelRF is a variable selection from random forests using both backwards variable elimination (for the selection of small sets of non-redundant variables) and selection based on the importance spectrum (somewhat similar to scree plots; for the selection of large, potentially highly-correlated variables). It is an R package for applied biomedical researchers, specially for exploratory work with microarray data. Because of the underlying technology used (combination of parallelization with web-based application) they are also of methodological interest to bioinformaticians and biostatisticians. varSelRF is also available as a web-based tool: GeneSrF, and is part of the suite ASTERIAS.
CMA / Classification for MicroArrays
Classifies construction and evaluation of microarrays data implementing most usual approaches. CMA offers an interface to a total of more than twenty different classifiers, seven univariate and multivariate variable selection methods, different evaluation schemes, and different measures of classification accuracy. The primary goal of CMA is to enable statisticians with limited experience on high-dimensional class prediction or biologists and bioinformaticians with statistical background to achieve such a demanding task on their own.
SET / Signature Evaluation Tool
Allows researchers to evaluate gene signatures based on expression datasets. SET provides a gene filtration threshold for gene identification between biological/clinical analyses and typical feature selection tools. The software supplies a signature evaluation platform that can adapt signatures from a variety of sources including third party analyses or candidates of interest that are deduced by biological knowledge. It can be useful for various evaluations in clinical research.
SCRDA / Shrunken Centroids Regularized Discriminant Analysis
Generalizes the idea of the “nearest shrunken centroids” (NSC) into the classical discriminant analysis. The SCRDA method is specially designed for classification problems in high dimension low sample size situations, for example, microarray data. Through both simulated data and real life data, this method performs very well in multivariate classification problems, often outperforms the Prediction Analysis of Microarrays (PAM) method (using the NSC algorithm) and can be as competitive as the support vector machines classifiers. It is also suitable for feature elimination purpose and can be used as gene selection method.
mclust
Enables model-based clustering, classification, and density estimation based on finite Gaussian mixture modelling. Mclust is an R package that provides a strategy for clustering, density estimation and discriminant analysis. It offers a variety of covariance structures obtained through eigenvalue decomposition, functions for performing single E and M steps and for simulating data for each available model. The software also includes additional ways of displaying and visualizing fitted models along with clustering, classification, and density estimation results.
GeNNet
Unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. GeNNet is an integrated transcriptome analysis platform that includes pre-loaded biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and geneset enrichment analysis. This platform integrates the analytical process of transcriptome data with graph database. It provides a comprehensive set of tools that would otherwise be challenging for non-expert users to install and use.
S-CART / sequential classification-and-regression-tree
Variables selection in the binary classification setting and compares it against the more sophisticated procedures using simulated and real biological data. S-CART outperforms stochastic search variable selection (SSVS) and random forest (RF) in both speed and detection accuracy. It can be useful on simulated data and in a control-treatment mouse study. It recapitulates the biological findings of the study using only a fraction of the original set of genes considered in the study's analysis.
MALA / MicroArray Logic Analyzer
Allows microarray gene expression analysis. MALA is based on an alternative clustering method and an effective classification approach to distinguish the different experimental samples. It follows five different steps to proceed: (1) discretization, (2) gene clustering, (3) feature selection, (4) formulas computation, and (5) classification. The tool was tested on a real microarray data set provided by the European Brain Research Institute in which control vs alzheimer diseased mice were spotted on a microarray.
geneClassifiers
Aims for easy accessible application of classifiers which have been published in literature using an ExpressionSet as input. geneClassifiers provides a method for running gene classifiers generating patient specific predictive outcomes. This package is intended to support and enable research. It is suitable only for datasets of at least 20 patients. It also performs a batch correction by applying a linear transformation of the probe-set means and standard deviations to the values observed in the classifiers’ training set.
CFS / Correlation-based Feature Selection
Measures correlation between nominal characteristics so that numerical characteristics are first discretized. CFS is a fully automatic algorithm that does not require the user to specify any thresholds or the number of features to be selected. It operates on the original feature space, meaning that any knowledge induced by a learning algorithm, using features selected by CFS, can be interpreted in terms of the original features, not in terms of a transformed space. This method is also a filter and does not incur high computational cost associated with repeatedly invoking a learning algorithm.
Ktspair / k-Top Scoring Pairs for Microarray Classification
A package which computes the k-TSP (k-Top Scoring Pairs). Ktspair uses pairs of genes to perform a classification which compares the relative ordering of the gene expressions within each profile. It ranks pairs of genes with respect to a score based on the sensibility and the specificity achieved by each pair. Ktspair selects the k pairs that achieved the maximum score with the restriction that a gene can appear in at most one pair. The number of pairs of genes is computed through crossvalidation or can be chosen by the user. Other functions related to the k-TSP are also available, for example the functions prediction, summary, plot, etc. can be found in the package.
[email protected] / MicroArray Classification BEnchmarking Tool on Host server
Obsolete
Performs microarray classification by providing prediction classification methods using randomizations of the benchmarking dataset. [email protected] offers two services: benchmarking and prediction. Benchmarking, the main service, involves selection and training of an optimal model based on the submitted benchmarking dataset and corresponding class labels. This model is then stored for immediate or later use on prospective data. By using the prediction service, [email protected] offers a way for later evaluation of prospective data by reusing an existing optimal prediction model. This tool was designed for the classification of patient samples, supposing microarray data are represented by an expression matrix characterized by high dimensionality in the sense of a small number of patients and a large number of gene expression levels for each patient.
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