{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T18:03:37Z","timestamp":1770141817969,"version":"3.49.0"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T00:00:00Z","timestamp":1697760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Regional Development"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Cancer is currently one of the most notorious diseases, with over 1 million deaths in the European Union alone in 2022. As each tumor can be composed of diverse cell types with distinct genotypes, cancer cells can acquire resistance to different compounds. Moreover, anticancer drugs can display severe side effects, compromising patient well-being. Therefore, novel strategies for identifying the optimal set of compounds to treat each tumor have become an important research topic in recent decades.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To address this challenge, we developed a novel drug response prediction algorithm called Drug Efficacy Leveraging Forked and Specialized networks (DELFOS). Our model learns from multi-omics data from over 65 cancer cell lines, as well as structural data from over 200 compounds, for the prediction of drug sensitivity. We also evaluated the benefits of incorporating single-cell expression data to predict drug response. DELFOS was validated using datasets with unseen cell lines or drugs and compared with other state-of-the-art algorithms, achieving a high prediction performance on several correlation and error metrics. Overall, DELFOS can effectively leverage multi-omics data for the prediction of drug responses in thousands of drug\u2013cell line pairs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The DELFOS pipeline and associated data are available at github.com\/MoreiraLAB\/delfos.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad645","type":"journal-article","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T17:54:43Z","timestamp":1697824483000},"source":"Crossref","is-referenced-by-count":6,"title":["DELFOS\u2014drug efficacy leveraging forked and specialized networks\u2014benchmarking scRNA-seq data in multi-omics-based prediction of cancer sensitivity"],"prefix":"10.1093","volume":"39","author":[{"given":"Luiz Felipe","family":"Piochi","sequence":"first","affiliation":[{"name":"Department of Life Sciences, University of Coimbra , Coimbra 3000-456, Portugal"},{"name":"CNC\u2014Center for Neuroscience and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra , Coimbra, Portugal"},{"name":"CIBB\u2014Center for Innovative Biomedicine and Biotechnology , Coimbra 3004-504, Portugal"}]},{"given":"Ant\u00f3nio J","family":"Preto","sequence":"additional","affiliation":[{"name":"CNC\u2014Center for Neuroscience and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra , Coimbra, Portugal"},{"name":"CIBB\u2014Center for Innovative Biomedicine and Biotechnology , Coimbra 3004-504, Portugal"},{"name":"PhD Programme in Experimental Biology and Biomedicine, Institute for Interdisciplinary Research (IIIUC), University of Coimbra , Coimbra 3030-789, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2970-5250","authenticated-orcid":false,"given":"Irina S","family":"Moreira","sequence":"additional","affiliation":[{"name":"Department of Life Sciences, 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