{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:12Z","timestamp":1772138052236,"version":"3.50.1"},"reference-count":61,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T00:00:00Z","timestamp":1654646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Government of Canada\u2019s New Frontiers in Research Fund","award":["NFRFE-2019-01290"],"award-info":[{"award-number":["NFRFE-2019-01290"]}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2019-04460"],"award-info":[{"award-number":["RGPIN-2019-04460"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"name":"McGill Initiative in Computational Medicine"},{"name":"G\u00e9nome Qu\u00e9bec, the Minist\u00e8re de l'\u00c9conomie et de l'Innovation du Qu\u00e9bec"},{"DOI":"10.13039\/501100019217","name":"IVADO","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100019217","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Canada First Research Excellence Fund and Oncopole"},{"DOI":"10.13039\/501100015631","name":"Merck Canada Inc.","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100015631","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fonds de Recherche du Qu\u00e9bec\u2014Sant\u00e9"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>The increasing number of publicly available databases containing drugs\u2019 chemical structures, their response in cell lines, and molecular profiles of the cell lines has garnered attention to the problem of drug response prediction. However, many existing methods do not fully leverage the information that is shared among cell lines and drugs with similar structure. As such, drug similarities in terms of cell line responses and chemical structures could prove to be useful in forming drug representations to improve drug response prediction accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present two deep learning approaches, BiG-DRP and BiG-DRP+, for drug response prediction. Our models take advantage of the drugs\u2019 chemical structure and the underlying relationships of drugs and cell lines through a bipartite graph and a heterogeneous graph convolutional network that incorporate sensitive and resistant cell line information in forming drug representations. Evaluation of our methods and other state-of-the-art models in different scenarios shows that incorporating this bipartite graph significantly improves the prediction performance. In addition, genes that contribute significantly to the performance of our models also point to important biological processes and signaling pathways. Analysis of predicted drug response of patients\u2019 tumors using our model revealed important associations between mutations and drug sensitivity, illustrating the utility of our model in pharmacogenomics studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>An implementation of the algorithms in Python is provided in https:\/\/github.com\/ddhostallero\/BiG-DRP.<\/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\/btac383","type":"journal-article","created":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T08:00:38Z","timestamp":1654675238000},"page":"3609-3620","source":"Crossref","is-referenced-by-count":27,"title":["Looking at the BiG picture: incorporating bipartite graphs in drug response prediction"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1611-8332","authenticated-orcid":false,"given":"David Earl","family":"Hostallero","sequence":"first","affiliation":[{"name":"Department \u00a0of Electrical and Computer Engineering, McGill University , Montreal, QC H3A 0E9, Canada"},{"name":"Mila, Quebec AI Institute , Montreal, QC H2S 3H1, Canada"}]},{"given":"Yihui","family":"Li","sequence":"additional","affiliation":[{"name":"Department \u00a0of Electrical and Computer Engineering, McGill University , Montreal, QC H3A 0E9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5108-4887","authenticated-orcid":false,"given":"Amin","family":"Emad","sequence":"additional","affiliation":[{"name":"Department \u00a0of Electrical and Computer Engineering, McGill University , Montreal, QC H3A 0E9, Canada"},{"name":"Mila, Quebec AI Institute , Montreal, QC H2S 3H1, Canada"},{"name":"The Rosalind and Morris Goodman Cancer Institute , Montreal, QC H3A 1A3, Canada"}]}],"member":"286","published-online":{"date-parts":[[2022,6,8]]},"reference":[{"key":"2023041405362238900_","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.semcancer.2019.07.009","article-title":"PI3K\/Akt\/mTOR inhibitors in cancer: at the bench and bedside","volume":"59","author":"Alzahrani","year":"2019","journal-title":"Semin. 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