Gene Selection for Multiclass Prediction by Weighted Fisher Criterion

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dc.contributor.authorXuan, Jianhuaen
dc.contributor.authorWang, Yueen
dc.contributor.authorDong, Yibinen
dc.contributor.authorFeng, Yuanjianen
dc.contributor.authorWang, Binen
dc.contributor.authorKhan, Javeden
dc.contributor.authorBakay, Mariaen
dc.contributor.authorWang, Zuyien
dc.contributor.authorPachman, Laurenen
dc.contributor.authorWinokur, Saraen
dc.contributor.authorChen, Yi-Wenen
dc.contributor.authorClarke, Roberten
dc.contributor.authorHoffman, Eric P.en
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2012-08-24T12:11:59Zen
dc.date.available2012-08-24T12:11:59Zen
dc.date.issued2007-07-10en
dc.date.updated2012-08-24T12:11:59Zen
dc.description.abstractGene expression profiling has been widely used to study molecular signatures of many diseases and to develop molecular diagnostics for disease prediction. Gene selection, as an important step for improved diagnostics, screens tens of thousands of genes and identifies a small subset that discriminates between disease types. A two-step gene selection method is proposed to identify informative gene subsets for accurate classification of multiclass phenotypes. In the first step, individually discriminatory genes (IDGs) are identified by using one-dimensional weighted Fisher criterion (wFC). In the second step, jointly discriminatory genes (JDGs) are selected by sequential search methods, based on their joint class separability measured by multidimensional weighted Fisher criterion (wFC). The performance of the selected gene subsets for multiclass prediction is evaluated by artificial neural networks (ANNs) and/or support vector machines (SVMs). By applying the proposed IDG/JDG approach to two microarray studies, that is, small round blue cell tumors (SRBCTs) and muscular dystrophies (MDs), we successfully identified a much smaller yet efficient set of JDGs for diagnosing SRBCTs and MDs with high prediction accuracies (96.9% for SRBCTs and 92.3% for MDs, resp.). These experimental results demonstrated that the two-step gene selection method is able to identify a subset of highly discriminative genes for improved multiclass prediction.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationEURASIP Journal on Bioinformatics and Systems Biology. 2007 Jul 10;2007(1):64628en
dc.identifier.doihttps://doi.org/10.1155/2007/64628en
dc.identifier.urihttp://hdl.handle.net/10919/18925en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.holderJianhua Xuan et al.; licensee BioMed Central Ltd.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleGene Selection for Multiclass Prediction by Weighted Fisher Criterionen
dc.title.serialEURASIP Journal on Bioinformatics and Systems Biologyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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