{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T14:42:46Z","timestamp":1781102566521,"version":"3.54.1"},"reference-count":0,"publisher":"IGI Global Scientific Publishing","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,7,1]]},"abstract":"<p>Biclustering is the unsupervised learning task of mining a data matrix for useful submatrices, for instance groups of genes that are co-expressed under particular biological conditions. As these submatrices are expected to partly overlap, a significant challenge in biclustering is to develop methods that are able to detect overlapping biclusters. The authors propose a probabilistic mixture modelling framework for biclustering biological data that lends itself to various data types and allows biclusters to overlap. Their framework is akin to the latent feature and mixture-of-experts model families, with inference and parameter estimation being performed via a variational expectation-maximization algorithm. The model compares favorably with competing approaches, both in a binary DNA copy number variation data set and in a miRNA expression data set, indicating that it may potentially be used as a general-problem solving tool in biclustering.<\/p>","DOI":"10.4018\/ijkdb.2016070102","type":"journal-article","created":{"date-parts":[[2016,11,18]],"date-time":"2016-11-18T14:15:15Z","timestamp":1479478515000},"page":"11-28","source":"Crossref","is-referenced-by-count":0,"title":["A Latent Feature Model Approach to Biclustering"],"prefix":"10.4018","volume":"6","author":[{"given":"Jos\u00e9","family":"Caldas","sequence":"first","affiliation":[{"name":"Aalto University, Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel","family":"Kaski","sequence":"additional","affiliation":[{"name":"Aalto University, Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","container-title":["International Journal of Knowledge Discovery in Bioinformatics"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=172003","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T12:20:06Z","timestamp":1654086006000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJKDB.2016070102"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2016,7,1]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2016,7]]}},"URL":"https:\/\/doi.org\/10.4018\/ijkdb.2016070102","relation":{},"ISSN":["1947-9115","1947-9123"],"issn-type":[{"value":"1947-9115","type":"print"},{"value":"1947-9123","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,7,1]]}}}