{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:31:42Z","timestamp":1784302302185,"version":"3.55.0"},"reference-count":0,"publisher":"Oxford University Press (OUP)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2003,5,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fuzzy partitioning method, Fuzzy C-means (FCM), to attribute cluster membership values to genes.<\/jats:p>\n               <jats:p>Results: A major problem in applying the FCM method for clustering microarray data is the choice of the fuzziness parameter m. We show that the commonly used value m = 2 is not appropriate for some data sets, and that optimal values for m vary widely from one data set to another. We propose an empirical method, based on the distribution of distances between genes in a given data set, to determine an adequate value for m. By setting threshold levels for the membership values, genes which are tigthly associated to a given cluster can be selected. Using a yeast cell cycle data set as an example, we show that this selection increases the overall biological significance of the genes within the cluster.<\/jats:p>\n               <jats:p>Availability: Supplementary text and Matlab functions are available at http:\/\/www-igbmc.u-strasbg.fr\/fcm\/<\/jats:p>\n               <jats:p>Contact: doulaye@titus.u-strasbg.fr<\/jats:p>\n               <jats:p>* To whom correspondence should be addressed.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btg119","type":"journal-article","created":{"date-parts":[[2003,5,21]],"date-time":"2003-05-21T17:52:52Z","timestamp":1053539572000},"page":"973-980","source":"Crossref","is-referenced-by-count":403,"title":["Fuzzy C-means method for clustering microarray data"],"prefix":"10.1093","volume":"19","author":[{"given":"Doulaye","family":"Demb\u00e9l\u00e9","sequence":"first","affiliation":[{"name":"Institut de G\u00e9n\u00e9tique et de Biologie Mol\u00e9culaire et Cellulaire, CNRS-IMSERM-ULP, BP 10142, 67404 Illkirch Cedex, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philippe","family":"Kastner","sequence":"additional","affiliation":[{"name":"Institut de G\u00e9n\u00e9tique et de Biologie Mol\u00e9culaire et Cellulaire, CNRS-IMSERM-ULP, BP 10142, 67404 Illkirch Cedex, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2003,5,22]]},"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/19\/8\/973\/48904226\/bioinformatics_19_8_973.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/19\/8\/973\/48904226\/bioinformatics_19_8_973.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T17:22:40Z","timestamp":1674667360000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/19\/8\/973\/235328"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2003,5,22]]},"references-count":0,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2003,5,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btg119","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2003,5,22]]},"published":{"date-parts":[[2003,5,22]]}}}