{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T02:26:56Z","timestamp":1780626416019,"version":"3.54.1"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T00:00:00Z","timestamp":1680739200000},"content-version":"vor","delay-in-days":5,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Government of Canada\u2019s New Frontiers in Research Fund"},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,4,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Combination therapies have emerged as a treatment strategy for cancers to reduce the probability of drug resistance and to improve outcomes. Large databases curating the results of many drug screening studies on preclinical cancer cell lines have been developed, capturing the synergistic and antagonistic effects of combination of drugs in different cell lines. However, due to the high cost of drug screening experiments and the sheer size of possible drug combinations, these databases are quite sparse. This necessitates the development of transductive computational models to accurately impute these missing values.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we developed MARSY, a deep-learning multitask model that incorporates information on the gene expression profile of cancer cell lines, as well as the differential expression signature induced by each drug to predict drug-pair synergy scores. By utilizing two encoders to capture the interplay between the drug pairs, as well as the drug pairs and cell lines, and by adding auxiliary tasks in the predictor, MARSY learns latent embeddings that improve the prediction performance compared to state-of-the-art and traditional machine-learning models. Using MARSY, we then predicted the synergy scores of 133\u00a0722 new drug-pair cell line combinations, which we have made available to the community as part of this study. Moreover, we validated various insights obtained from these novel predictions using independent studies, confirming the ability of MARSY in making accurate novel predictions.<\/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 and cleaned input datasets are provided in https:\/\/github.com\/Emad-COMBINE-lab\/MARSY.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad177","type":"journal-article","created":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T08:44:10Z","timestamp":1680770650000},"source":"Crossref","is-referenced-by-count":46,"title":["MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores"],"prefix":"10.1093","volume":"39","author":[{"given":"Mohamed Reda","family":"El Khili","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, McGill University , Montreal, QC H3A 0E9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9247-100X","authenticated-orcid":false,"given":"Safyan Aman","family":"Memon","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, McGill University , Montreal, QC H3A 0E9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5108-4887","authenticated-orcid":false,"given":"Amin","family":"Emad","sequence":"additional","affiliation":[{"name":"Department of 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, 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