{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T17:11:47Z","timestamp":1784913107718,"version":"3.55.0"},"reference-count":15,"publisher":"Oxford University Press (OUP)","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: The result of a typical microarray experiment is a long list of genes with corresponding expression measurements. This list is only the starting point for a meaningful biological interpretation. Modern methods identify relevant biological processes or functions from gene expression data by scoring the statistical significance of predefined functional gene groups, e.g. based on Gene Ontology (GO). We develop methods that increase the explanatory power of this approach by integrating knowledge about relationships between the GO terms into the calculation of the statistical significance.<\/jats:p>\n               <jats:p>Results: We present two novel algorithms that improve GO group scoring using the underlying GO graph topology. The algorithms are evaluated on real and simulated gene expression data. We show that both methods eliminate local dependencies between GO terms and point to relevant areas in the GO graph that remain undetected with state-of-the-art algorithms for scoring functional terms. A simulation study demonstrates that the new methods exhibit a higher level of detecting relevant biological terms than competing methods.<\/jats:p>\n               <jats:p>Availability: topgo.bioinf.mpi-inf.mpg.de<\/jats:p>\n               <jats:p>Contact: \u00a0alexa@mpi-sb.mpg.de<\/jats:p>\n               <jats:p>Supplementary Information: Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btl140","type":"journal-article","created":{"date-parts":[[2006,4,11]],"date-time":"2006-04-11T00:33:49Z","timestamp":1144715629000},"page":"1600-1607","source":"Crossref","is-referenced-by-count":2042,"title":["Improved scoring of functional groups from gene expression data by decorrelating GO graph structure"],"prefix":"10.1093","volume":"22","author":[{"given":"Adrian","family":"Alexa","sequence":"first","affiliation":[{"name":"Max-Planck-Institute for Informatics \u00a0 Stuhlsatzenhausweg 85, D-66123 Saarbr\u00fccken, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00f6rg","family":"Rahnenf\u00fchrer","sequence":"additional","affiliation":[{"name":"Max-Planck-Institute for Informatics \u00a0 Stuhlsatzenhausweg 85, D-66123 Saarbr\u00fccken, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Lengauer","sequence":"additional","affiliation":[{"name":"Max-Planck-Institute for Informatics \u00a0 Stuhlsatzenhausweg 85, D-66123 Saarbr\u00fccken, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2006,4,10]]},"reference":[{"key":"2023012408335262800_b1","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1093\/bioinformatics\/btg455","article-title":"FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes","volume":"20","author":"Al-Shahrour","year":"2004","journal-title":"Bioinformatics"},{"key":"2023012408335262800_b2","first-page":"25","article-title":"Gene ontology: tool for the unification of biology","volume":"25","author":"Ashburner","year":"2000","journal-title":"The Gene Ontology Consortium. Nat. Genet."},{"key":"2023012408335262800_b3","doi-asserted-by":"crossref","first-page":"3353","DOI":"10.1093\/bioinformatics\/bth405","article-title":"A graph-theoretic approach to testing associations between disparate sources of functional genomics data","volume":"20","author":"Balasubramanian","year":"2004","journal-title":"Bioinformatics"},{"key":"2023012408335262800_b4","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1093\/bioinformatics\/bth088","article-title":"GOstat: find statistically overrepresented Gene Ontologies within a group of genes","volume":"20","author":"Beissbarth","year":"2004","journal-title":"Bioinformatics"},{"key":"2023012408335262800_b5","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1214\/aos\/1013699998","article-title":"The control of the false discovery rate in multiple testing under dependency","volume":"29","author":"Benjamini","year":"2001","journal-title":"Ann. 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