{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T13:28:50Z","timestamp":1781530130219,"version":"3.54.1"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011851","name":"Kom op tegen Kanker","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011851","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioinform."],"abstract":"<jats:p>In precision oncology, therapy stratification is done based on the patients\u2019 tumor molecular profile. Modeling and prediction of the drug response for a given tumor molecular type will further improve therapeutic decision-making for cancer patients. Indeed, deep learning methods hold great potential for drug sensitivity prediction, but a major problem is that these models are black box algorithms and do not clarify the mechanisms of action. This puts a limitation on their clinical implementation. To address this concern, many recent studies attempt to overcome these issues by developing interpretable deep learning methods that facilitate the understanding of the logic behind the drug response prediction. In this review, we discuss strengths and limitations of recent approaches, and suggest future directions that could guide further improvement of interpretable deep learning in drug sensitivity prediction in cancer research.<\/jats:p>","DOI":"10.3389\/fbinf.2022.1036963","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T16:27:05Z","timestamp":1668702425000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Opportunities and challenges in interpretable deep learning for drug sensitivity prediction of cancer cells"],"prefix":"10.3389","volume":"2","author":[{"given":"Bikash Ranjan","family":"Samal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens Uwe","family":"Loers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vanessa","family":"Vermeirssen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katleen","family":"De Preter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,11,17]]},"reference":[{"key":"B41","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1808.04260","article-title":"iNNvestigate neural networks","author":"Alber","year":"2018","journal-title":"arXiv"},{"key":"B1","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1016\/j.tig.2020.03.005","article-title":"Opening the black box: Interpretable machine learning for geneticists","volume":"36","author":"Azodi","year":"2020","journal-title":"Trends Genet."},{"key":"B2","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","article-title":"Clinical-grade computational pathology using weakly supervised deep learning on whole slide images","volume":"25","author":"Campanella","year":"2019","journal-title":"Nat. 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