{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T13:00:52Z","timestamp":1791291652009,"version":"4.1.0"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2016,10,3]],"date-time":"2016-10-03T00:00:00Z","timestamp":1475452800000},"content-version":"vor","delay-in-days":845,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Motivation: Most existing methods for predicting causal disease genes rely on specific type of evidence, and are therefore limited in terms of applicability. More often than not, the type of evidence available for diseases varies\u2014for example, we may know linked genes, keywords associated with the disease obtained by mining text, or co-occurrence of disease symptoms in patients. Similarly, the type of evidence available for genes varies\u2014for example, specific microarray probes convey information only for certain sets of genes. In this article, we apply a novel matrix-completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations; it combines multiple types of evidence (features) for diseases and genes to learn latent factors that explain the observed gene\u2013disease associations. We construct features from different biological sources such as microarray expression data and disease-related textual data. A crucial advantage of the method is that it is inductive; it can be applied to diseases not seen at training time, unlike traditional matrix-completion approaches and network-based inference methods that are transductive.<\/jats:p>\n                  <jats:p>Results: Comparison with state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database shows that the proposed approach is substantially better\u2014it has close to one-in-four chance of recovering a true association in the top 100 predictions, compared to the recently proposed Catapult method (second best) that has &amp;lt;15% chance. We demonstrate that the inductive method is particularly effective for a query disease with no previously known gene associations, and for predicting novel genes, i.e. genes that are previously not linked to diseases. Thus the method is capable of predicting novel genes even for well-characterized diseases. We also validate the novelty of predictions by evaluating the method on recently reported OMIM associations and on associations recently reported in the literature.<\/jats:p>\n                  <jats:p>Availability: Source code and datasets can be downloaded from http:\/\/bigdata.ices.utexas.edu\/project\/gene-disease.<\/jats:p>\n                  <jats:p>Contact: \u00a0naga86@cs.utexas.edu<\/jats:p>","DOI":"10.1093\/bioinformatics\/btu269","type":"journal-article","created":{"date-parts":[[2014,6,16]],"date-time":"2014-06-16T17:55:09Z","timestamp":1402941309000},"page":"i60-i68","source":"Crossref","is-referenced-by-count":259,"title":["Inductive matrix completion for predicting gene\u2013disease associations"],"prefix":"10.1093","volume":"30","author":[{"given":"Nagarajan","family":"Natarajan","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Texas at Austin, Austin, TX 78712, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inderjit S.","family":"Dhillon","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Texas at Austin, Austin, TX 78712, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2014,6,11]]},"reference":[{"key":"2023012711105991400_btu269-B1","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1186\/1471-2105-6-55","article-title":"Speeding disease gene discovery by sequence based candidate prioritization","volume":"6","author":"Adie","year":"2005","journal-title":"BMC Bioinform."},{"key":"2023012711105991400_btu269-B2","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/nrg2918","article-title":"Network medicine: a network-based approach to human disease","volume":"12","author":"Barab\u00e1si","year":"2011","journal-title":"Nat. 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