{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T18:31:30Z","timestamp":1778869890457,"version":"3.51.4"},"reference-count":71,"publisher":"Oxford University Press (OUP)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Leveraging the large compendium of genomic data to predict biomedical pathways and specific mechanisms of protein interactions genome-wide in metazoan organisms has been challenging. In contrast to unicellular organisms, biological and technical variation originating from diverse tissues and cell-lineages is often the largest source of variation in metazoan data compendia. Therefore, a new computational strategy accounting for the tissue heterogeneity in the functional genomic data is needed to accurately translate the vast amount of human genomic data into specific interaction-level hypotheses.<\/jats:p>\n               <jats:p>Results: We developed an integrated, scalable strategy for inferring multiple human gene interaction types that takes advantage of data from diverse tissue and cell-lineage origins. Our approach specifically predicts both the presence of a functional association and also the most likely interaction type among human genes or its protein products on a whole-genome scale. We demonstrate that directly incorporating tissue contextual information improves the accuracy of our predictions, and further, that such genome-wide results can be used to significantly refine regulatory interactions from primary experimental datasets (e.g. ChIP-Seq, mass spectrometry).<\/jats:p>\n               <jats:p>Availability and implementation: An interactive website hosting all of our interaction predictions is publically available at http:\/\/pathwaynet.princeton.edu. Software was implemented using the open-source Sleipnir library, which is available for download at https:\/\/bitbucket.org\/libsleipnir\/libsleipnir.bitbucket.org.<\/jats:p>\n               <jats:p>Contact: \u00a0ogt@cs.princeton.edu<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btu786","type":"journal-article","created":{"date-parts":[[2014,11,28]],"date-time":"2014-11-28T15:35:07Z","timestamp":1417188907000},"page":"1093-1101","source":"Crossref","is-referenced-by-count":96,"title":["Tissue-aware data integration approach for the inference of pathway interactions in metazoan organisms"],"prefix":"10.1093","volume":"31","author":[{"given":"Christopher Y.","family":"Park","sequence":"first","affiliation":[{"name":"1 Department of Computer Science, Princeton University, Princeton, NJ 08544, USA, 2Lewis-Sigler Institute for Integrative Genomics, Princeton 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