{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T14:58:43Z","timestamp":1784041123785,"version":"3.55.0"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1012905","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T00:00:00Z","timestamp":1744070400000}}],"reference-count":83,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T00:00:00Z","timestamp":1743379200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372286"],"award-info":[{"award-number":["62372286"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Science and Technology Innovation Action Planning","award":["20dz1203800"],"award-info":[{"award-number":["20dz1203800"]}]},{"name":"Research and Development Planning in Key Areas of Guangdong Province","award":["2021B0202070001"],"award-info":[{"award-number":["2021B0202070001"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient\u2019s response to drug and a thorough understanding of differences in drug responses among individuals continue to pose significant challenges. This study introduced a deep learning model PASO, which integrated transformer encoder, multi-scale convolutional networks and attention mechanisms to predict the sensitivity of cell lines to anticancer drugs, based on the omics data of cell lines and the SMILES representations of drug molecules. First, we use statistical methods to compute the differences in gene expression, gene mutation, and gene copy number variations between within and outside biological pathways, and utilized these pathway difference values as cell line features, combined with the drugs\u2019 SMILES chemical structure information as inputs to the model. Then the model integrates various deep learning technologies multi-scale convolutional networks and transformer encoder to extract the properties of drug molecules from different perspectives, while an attention network is devoted to learning complex interactions between the omics features of cell lines and the aforementioned properties of drug molecules. Finally, a multilayer perceptron (MLP) outputs the final predictions of drug response. Our model exhibits higher accuracy in predicting the sensitivity to anticancer drugs comparing with other methods proposed recently. It is found that PARP inhibitors, and Topoisomerase I inhibitors were particularly sensitive to SCLC when analyzing the drug response predictions for lung cancer cell lines. Additionally, the model is capable of highlighting biological pathways related to cancer and accurately capturing critical parts of the drug\u2019s chemical structure. We also validated the model\u2019s clinical utility using clinical data from The Cancer Genome Atlas. In summary, the PASO model suggests potential as a robust support in individualized cancer treatment. Our methods are implemented in Python and are freely available from GitHub (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/queryang\/PASO\" xlink:type=\"simple\">https:\/\/github.com\/queryang\/PASO<\/jats:ext-link>).<\/jats:p>","DOI":"10.1371\/journal.pcbi.1012905","type":"journal-article","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T20:54:43Z","timestamp":1743454483000},"page":"e1012905","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":22,"title":["Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques"],"prefix":"10.1371","volume":"21","author":[{"given":"Yang","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8906-8727","authenticated-orcid":true,"given":"Yufang","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,3,31]]},"reference":[{"issue":"5","key":"pcbi.1012905.ref001","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1038\/nrc3261","article-title":"Intra-tumour heterogeneity: a looking glass for cancer?","volume":"12","author":"A Marusyk","year":"2012","journal-title":"Nat Rev Cancer"},{"issue":"1","key":"pcbi.1012905.ref002","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.pharmthera.2012.01.001","article-title":"A 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