litlas
Log inCreate account

Minimum Redundancy Feature Selection from Microarray Gene Expression Data

Authors: Chris H. Q. Ding, Hanchuan Peng - Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 2003 cited by 2,794

How to selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the top-ranked genes. We observe that feature sets so obtained have certain redundancy and study methods to minimize it. We propose a minimum redundancy - maximum relevance (MRMR) feature selection framework. Genes selected via MRMR provide a more balanced coverage of the space and capture broader characteristics of phenotypes. They lead to significantly improved class predictions in extensive experiments on 6 gene expression data sets: NCI, Lymphoma, Lung, Child Leukemia, Leukemia, and Colon. Improvements are observed consistently among 4 classification methods: Naive Bayes, Linear discriminant analysis, Logistic regression, and Support vector machines. SUPPLIMENTARY: The top 60 MRMR genes for each of the datasets are listed in http://crd.lbl.gov/~cding/MRMR/. More information related to MRMR methods can be found at http://www.hpeng.net/.

Selection of RelevantFeatures in Machine…Selection of Relevant Features in Machine Learning.Toward Optimal FeatureSelectionToward Optimal Feature SelectionA Tutorial on SupportVector Machines for…A Tutorial on Support Vector Machines for Pattern RecognitionSupport vector machineclassification and…Support vector machine classification and validation of cancer tissue samples using microarray expression dataDistinct types ofdiffuse large B-cell…Distinct types of diffuse large B-cell lymphoma identified by gene expression profilingBiomarker Identificationby Feature WrappersBiomarker Identification by Feature WrappersFeature selection forhigh-dimensional genomi…Feature selection for high-dimensional genomic microarray dataMulti-class protein foldrecognition using…Multi-class protein fold recognition using support vector machines and neural networksTumor classification bypartial least squares…Tumor classification by partial least squares using microarray gene expression dataMulti-class cancerclassification via…Multi-class cancer classification via partial least squares with gene expression profilesClassification, subtypediscovery, and…Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profilingAnalysis of geneexpression profiles…Analysis of gene expression profiles: class discovery and leaf orderingAn Entropy-based geneselection method for…An Entropy-based gene selection method for cancer classification using microarray dataA stable gene selectionin microarray data…A stable gene selection in microarray data analysisInformation-TheoreticInference of Large…Information-Theoretic Inference of Large Transcriptional Regulatory NetworksInformation-TheoreticFeature Selection in…Information-Theoretic Feature Selection in Microarray Data Using Variable Complementarityminet: A R/BioconductorPackage for Inferring…minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual InformationOptimization methodbased extreme learning…Optimization method based extreme learning machine for classificationFeature subset selectionwith cumulate…Feature subset selection with cumulate conditional mutual information minimizationMicroarray geneexpression…Microarray gene expression classification with few genes: Criteria to combine attribute selection and classification methodsOnline feature selectionfor mining big dataOnline feature selection for mining big dataIncremental wrapperbased gene selection…Incremental wrapper based gene selection with Markov blanketRecent advances andemerging challenges of…Recent advances and emerging challenges of feature selection in the context of big dataFeature selection withmulti-view data: A…Feature selection with multi-view data: A surveyMinimum RedundancyFeature Selection from…Minimum Redundancy Feature Selection from Microarray Gene Expression DataEarlier referencesFocus paperCiting papersOlderNewer

Click a node to pin it, click the empty canvas to go back to this paper, or hover to preview. Open a node’s page from its title.