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Classification software tools | Gene expression microarray data analysis

Gene expression profiling based on microarray technology has been applied widely on monitoring global transcriptome changes in biological samples. In cancer research, one of the major microarray applications is to identify genes, or features, whose expression patterns can discriminate samples with distinct states (usually defined by the phenotype of samples such as primary or metastatic tumour).

Source text:
(Jen et al., 2008) Signature Evaluation Tool (SET): a Java-based tool to evaluate and visualize the sample discrimination abilities of gene expression signatures. BMC Bioinformatics.

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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.
CARMAweb / Comprehensive R-based Microarray Analysis web service
Provides several unique features in a modular and flexible system for the analysis of microarray data. The design and modular conception of CARMAweb allows the use of the different analysis modules either individually or combined into an analytical pipeline. CARMAweb performs (i) data preprocessing (background correction, quality control and normalization), (ii) detection of differentially expressed genes, (iii) cluster analysis, (iv) dimension reduction and (v) visualization, classification, and Gene Ontology-term analysis.
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.
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.
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.
MRMR-mv / Min-Redundancy and Maximum Relevance for the Multi-View settings
Provides a multi-view feature selection algorithm. MRMR-mv is a maximum relevance and minimum redundancy based multi-view feature selection method. It enables views to be treated unequally and jointly performs feature selection in a view-aware manner that allows features from all views to be present in the set of selected features. It was used to build predictive models for ovarian cancer survival using multi-omics data derived from the Cancer Genome Atlas (TCGA).
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.
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