{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T07:33:50Z","timestamp":1762673630026},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"21","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2007,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Genome-wide experiments only rarely show resounding success in yielding genes associated with complex polygenic disorders. We evaluate 49 obesity-related genome-wide experiments with publicly available findings including microarray, genetics, proteomics and gene knock-down from human, mouse, rat and worm, in terms of their ability to rediscover a comprehensive set of genes previously found to be causally associated or having variants associated with obesity.<\/jats:p><jats:p>Results: Individual experiments show poor predictive ability for rediscovering known obesity-associated genes. We show that intersecting the results of experiments significantly improves the sensitivity, specificity and precision of the prediction of obesity-associated genes. We create an integrative model that statistically significantly outperforms all 49 individual genome-wide experiments. We find that genes known to be associated with obesity are significantly implicated in more obesity-related experiments and use this to provide a list of genes that we predict to have the highest likelihood of association for obesity. The approach described here can include any number and type of genome-wide experiments and might be useful for other complex polygenic disorders as well.<\/jats:p><jats:p>Contact: \u00a0abutte@stanford.edu<\/jats:p><jats:p>Supplementary information: Available online and at http:\/\/buttelab.stanford.edu\/doku.php?id=public:obesityintegration<\/jats:p>","DOI":"10.1093\/bioinformatics\/btm483","type":"journal-article","created":{"date-parts":[[2007,10,7]],"date-time":"2007-10-07T00:24:16Z","timestamp":1191716656000},"page":"2910-2917","source":"Crossref","is-referenced-by-count":42,"title":["Evaluation and integration of 49 genome-wide experiments and the prediction of previously unknown obesity-related genes"],"prefix":"10.1093","volume":"23","author":[{"given":"Sangeeta B.","family":"English","sequence":"first","affiliation":[{"name":"Department of Medicine and Department of Pediatrics, Stanford Medical Informatics, Stanford University School of Medicine, and Lucile Packard Children's Hospital, Stanford, CA 94305, USA"}]},{"given":"Atul J.","family":"Butte","sequence":"additional","affiliation":[{"name":"Department of Medicine and Department of Pediatrics, Stanford Medical Informatics, Stanford University School of Medicine, and Lucile Packard Children's Hospital, Stanford, CA 94305, USA"}]}],"member":"286","published-online":{"date-parts":[[2007,10,5]]},"reference":[{"key":"2023041107264690600_","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1038\/nbt1203","article-title":"Gene prioritization through genomic data fusion","volume":"24","author":"Aerts","year":"2006","journal-title":"Nat. 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