{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T23:35:16Z","timestamp":1789601716323,"version":"build-2803163510"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T00:00:00Z","timestamp":1647993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Technology Innovation Program","award":["20016417"],"award-info":[{"award-number":["20016417"]}]},{"name":"Ministry of Trade"},{"name":"Industry & Energy (MOTIE"},{"name":"Gachon University Gil Medical Center","award":["FRD2019-11"],"award-info":[{"award-number":["FRD2019-11"]}]},{"name":"Gachon University research fund of 2020","award":["GCU-202008430003"],"award-info":[{"award-number":["GCU-202008430003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Predicting drug response is critical for precision medicine. Diverse methods have predicted drug responsiveness, as measured by the half-maximal drug inhibitory concentration (IC50), in cultured cells. Although IC50s are continuous, traditional prediction models have dealt mainly with binary classification of responsiveness. However, since there are few regression-based IC50 predictions, comprehensive evaluations of regression-based IC50 prediction models, including machine learning (ML) and deep learning (DL), for diverse data types and dataset sizes, have not been addressed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Here, we constructed 11 input data settings, including multi-omics settings, with varying dataset sizes, then evaluated the performance of regression-based ML and DL models to predict IC50s. DL models considered two convolutional neural network architectures: CDRScan and residual neural network (ResNet). ResNet was introduced in regression-based DL models for predicting drug response for the first time. As a result, DL models performed better than ML models in all the settings. Also, ResNet performed better than or comparable to CDRScan and ML models in all settings.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The data underlying this article are available in GitHub at https:\/\/github.com\/labnams\/IC50evaluation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac177","type":"journal-article","created":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T12:22:00Z","timestamp":1647951720000},"page":"2810-2817","source":"Crossref","is-referenced-by-count":41,"title":["A comprehensive evaluation of regression-based drug responsiveness prediction models, using cell viability inhibitory concentrations (IC50 values)"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0203-6069","authenticated-orcid":false,"given":"Aron","family":"Park","sequence":"first","affiliation":[{"name":"Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University , Incheon 21999, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minjae","family":"Joo","sequence":"additional","affiliation":[{"name":"Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University , Incheon 21999, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyungdoc","family":"Kim","sequence":"additional","affiliation":[{"name":"VUNO Inc. , Seoul 06541, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4087-4661","authenticated-orcid":false,"given":"Won-Joon","family":"Son","sequence":"additional","affiliation":[{"name":"Samsung Advanced Institute of Technology, Samsung Electronics , Suwon, Gyeonggi-do 16678, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"GyuTae","family":"Lim","sequence":"additional","affiliation":[{"name":"Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB) , Daejeon 34141, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7437-1846","authenticated-orcid":false,"given":"Jinhyuk","family":"Lee","sequence":"additional","affiliation":[{"name":"Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB) , Daejeon 34141, Korea"},{"name":"Department of Bioinformatics, University of Sciences and Technology , Daejeon 34113, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jung Ho","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University School of Medicine , Incheon 21565, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dae Ho","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University , Incheon 21999, Korea"},{"name":"Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University School of Medicine , Incheon 21565, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0966-7915","authenticated-orcid":false,"given":"Seungyoon","family":"Nam","sequence":"additional","affiliation":[{"name":"Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University , Incheon 21999, Korea"},{"name":"AI Convergence Center for Medical Science, Department of Genome Medicine and Science, Gachon University Gil Medical Center, Gachon University College of Medicine , Incheon 21565, Korea"},{"name":"Department of Life Sciences, Gachon University , Seongnam, Gyeonggi-do 13120, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,3,23]]},"reference":[{"key":"2023020109131294400_btac177-B1","doi-asserted-by":"crossref","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":"2023020109131294400_btac177-B2","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.3389\/fgene.2019.01041","article-title":"Paclitaxel response can be predicted with interpretable multi-variate classifiers exploiting DNA-methylation and miRNA data","volume":"10","author":"Bomane","year":"2019","journal-title":"Front. 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