{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T14:35:28Z","timestamp":1761662128805},"reference-count":0,"publisher":"Oxford University Press (OUP)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2004,8,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Grouping genes having similar expression patterns is called gene clustering, which has been proved to be a useful tool for extracting underlying biological information of gene expression data. Many clustering procedures have shown success in microarray gene clustering; most of them belong to the family of heuristic clustering algorithms. Model-based algorithms are alternative clustering algorithms, which are based on the assumption that the whole set of microarray data is a finite mixture of a certain type of distributions with different parameters. Application of the model-based algorithms to unsupervised clustering has been reported. Here, for the first time, we demonstrated the use of the model-based algorithm in supervised clustering of microarray data.<\/jats:p>\n               <jats:p>Results: We applied the proposed methods to real gene expression data and simulated data. We showed that the supervised model-based algorithm is superior over the unsupervised method and the support vector machines (SVM) method.<\/jats:p>\n               <jats:p>Availability: The program written in the SAS language implementing methods I\u2013III in this report is available upon request. The software of SVMs is available in the website http:\/\/svm.sdsc.edu\/cgi-bin\/nph-SVMsubmit.cgi<\/jats:p>","DOI":"10.1093\/bioinformatics\/bth177","type":"journal-article","created":{"date-parts":[[2004,3,30]],"date-time":"2004-03-30T01:34:06Z","timestamp":1080610446000},"page":"1905-1913","source":"Crossref","is-referenced-by-count":69,"title":["Supervised cluster analysis for microarray data based on multivariate Gaussian mixture"],"prefix":"10.1093","volume":"20","author":[{"given":"Yi","family":"Qu","sequence":"first","affiliation":[{"name":"Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shizhong","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2004,3,25]]},"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/20\/12\/1905\/48905810\/bioinformatics_20_12_1905.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/20\/12\/1905\/48905810\/bioinformatics_20_12_1905.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T16:32:24Z","timestamp":1674664344000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/20\/12\/1905\/218152"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2004,3,25]]},"references-count":0,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2004,8,12]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bth177","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2004,8,12]]},"published":{"date-parts":[[2004,3,25]]}}}