{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T01:44:34Z","timestamp":1768095874346,"version":"3.49.0"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2007,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Most gene-expression based studies aim to identify genes with the capability of distinguishing different phenotypes. Although analysis at the genomic level is important, results of the molecular\/cellular level are essential for understanding biological mechanisms. To deliver molecular\/cellular-level results, a two-stage scheme is widely employed. This scheme just evaluates biological processes\/molecular activities individually, totally overlooking the relationship between processes\/activities. This treatment conflicts with the fact that most biological processes\/molecular activities do not work alone. In order to deliver improved results, this shortcoming should be addressed.<\/jats:p><jats:p>Results: We design a selection model from a novel perspective to directly detect important gene functional categories (each category represents a cellular process or a molecular activity). More importantly, the correlations between gene categories are considered. Contributed by this capability, the proposed method shows its advantages over others.<\/jats:p><jats:p>Availability: the source code in Matlab is accessible via http:\/\/www.ee.cityu.edu.hk\/~twschow\/category_selection\/category_selection.htm<\/jats:p><jats:p>Contact: \u00a0ifkorf@ucdavis.edu<\/jats:p><jats:p>Supplementary information: \u00a0http:\/\/www.ee.cityu.edu.hk\/~twschow\/category_selection\/category_selection.htm<\/jats:p>","DOI":"10.1093\/bioinformatics\/btm141","type":"journal-article","created":{"date-parts":[[2007,4,27]],"date-time":"2007-04-27T00:29:34Z","timestamp":1177633774000},"page":"1503-1510","source":"Crossref","is-referenced-by-count":11,"title":["Identifying the biologically relevant gene categories based on gene expression and biological data: an example on prostate cancer"],"prefix":"10.1093","volume":"23","author":[{"given":"D.","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tommy W. 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