{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T08:19:16Z","timestamp":1793089156828,"version":"4.5.0"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000196","name":"Canada Foundation for Innovation","doi-asserted-by":"publisher","award":["33440"],"award-info":[{"award-number":["33440"]}],"id":[{"id":"10.13039\/501100000196","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"The Canadian Institutes of Health Research","doi-asserted-by":"crossref","award":["PJT-153073"],"award-info":[{"award-number":["PJT-153073"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002655","name":"Terry Fox Foundation","doi-asserted-by":"publisher","award":["201012TFF"],"award-info":[{"award-number":["201012TFF"]}],"id":[{"id":"10.13039\/501100002655","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science and Engineering Research Council of Canada","award":["R611347"],"award-info":[{"award-number":["R611347"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose MOLI, a multi-omics late integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding sub-networks to learn features for each omics type, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. The former makes the representations of responder samples more similar to each other and different from the non-responders, and the latter makes this representation predictive of the response values. We validate MOLI on in vitro and in vivo datasets for five chemotherapy agents and two targeted therapeutics. Compared to state-of-the-art single-omics and early integration multi-omics methods, MOLI achieves higher prediction accuracy in external validations. Moreover, a significant improvement in MOLI\u2019s performance is observed for targeted drugs when training on a pan-drug input, i.e. using all the drugs with the same target compared to training only on drug-specific inputs. MOLI\u2019s high predictive power suggests it may have utility in precision oncology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>https:\/\/github.com\/hosseinshn\/MOLI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz318","type":"journal-article","created":{"date-parts":[[2019,6,5]],"date-time":"2019-06-05T23:19:10Z","timestamp":1559776750000},"page":"i501-i509","source":"Crossref","is-referenced-by-count":392,"title":["MOLI: multi-omics late integration with deep neural networks for drug response prediction"],"prefix":"10.1093","volume":"35","author":[{"given":"Hossein","family":"Sharifi-Noghabi","sequence":"first","affiliation":[{"name":"School of Computing Science, Simon Fraser University, Burnaby, BC, Canada"},{"name":"Vancouver Prostate Centre, Vancouver, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga","family":"Zolotareva","sequence":"additional","affiliation":[{"name":"International Research Training Group Computational Methods for the Analysis of the Diversity and Dynamics of Genomes and Genome Informatics, Faculty of Technology and Center for Biotechnology, Bielefeld University, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Colin C","family":"Collins","sequence":"additional","affiliation":[{"name":"Vancouver Prostate Centre, Vancouver, BC, Canada"},{"name":"Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Ester","sequence":"additional","affiliation":[{"name":"School of Computing Science, Simon Fraser University, Burnaby, BC, Canada"},{"name":"Vancouver Prostate Centre, Vancouver, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,7,5]]},"reference":[{"key":"2023062712361036600_btz318-B1","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1093\/bioinformatics\/btx766","article-title":"Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach","volume":"34","author":"Ali","year":"2018","journal-title":"Bioinformatics"},{"key":"2023062712361036600_btz318-B2","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1146\/annurev-pathol-020712-163923","article-title":"Cellular heterogeneity and molecular evolution in cancer","volume":"8","author":"Almendro","year":"2013","journal-title":"Ann. 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