{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T05:53:07Z","timestamp":1781416387667,"version":"3.54.1"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:00:00Z","timestamp":1743638400000},"content-version":"vor","delay-in-days":33,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012728","name":"Frederick National Laboratory for Cancer Research","doi-asserted-by":"publisher","award":["75N91019D00024"],"award-info":[{"award-number":["75N91019D00024"]}],"id":[{"id":"10.13039\/100012728","id-type":"DOI","asserted-by":"publisher"}]},{"name":"U.S. Department of Energy (DOE) and the National Cancer Institute (NCI) of the National Institutes of Health, Cancer Moonshot","award":["75N91019F00134"],"award-info":[{"award-number":["75N91019F00134"]}]},{"name":"National Cancer Institute, National Institutes of Health","award":["HHSN261200800001E"],"award-info":[{"award-number":["HHSN261200800001E"]}]},{"name":"Leidos Biomedical Research, Inc.","award":["A21154"],"award-info":[{"award-number":["A21154"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Drug response prediction (DRP) methods tackle the complex task of associating the effectiveness of small molecules with the specific genetic makeup of the patient. Anti-cancer DRP is a particularly challenging task requiring costly experiments as underlying pathogenic mechanisms are broad and associated with multiple genomic pathways. The scientific community has exerted significant efforts to generate public drug screening datasets, giving a path to various machine learning models that attempt to reason over complex data space of small compounds and biological characteristics of tumors. However, the data depth is still lacking compared to application domains like computer vision or natural language processing domains, limiting current learning capabilities. To combat this issue and improves the generalizability of the DRP models, we are exploring strategies that explicitly address the imbalance in the DRP datasets. We reframe the problem as a multi-objective optimization across multiple drugs to maximize deep learning model performance. We implement this approach by constructing Multi-Objective Optimization Regularized by Loss Entropy loss function and plugging it into a Deep Learning model. We demonstrate the utility of proposed drug discovery methods and make suggestions for further potential application of the work to achieve desirable outcomes in the healthcare field.<\/jats:p>","DOI":"10.1093\/bib\/bbaf134","type":"journal-article","created":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T09:24:00Z","timestamp":1743585840000},"source":"Crossref","is-referenced-by-count":5,"title":["Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3336-0534","authenticated-orcid":false,"given":"Oleksandr","family":"Narykov","sequence":"first","affiliation":[{"name":"Computing, Environment and Life Sciences, Argonne National Laboratory , 9700 S Cass Ave, Lemont, IL 60439 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yitan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Computing, Environment and Life Sciences, Argonne National Laboratory , 9700 S Cass Ave, Lemont, IL 60439 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Brettin","sequence":"additional","affiliation":[{"name":"Computing, Environment and Life Sciences, Argonne National Laboratory , 9700 S Cass Ave, Lemont, IL 60439 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yvonne A","family":"Evrard","sequence":"additional","affiliation":[{"name":"Leidos Biomedical Research, Frederick National Laboratory for Cancer Research , 8560 Progress Drive, Frederick, MD 21702 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Partin","sequence":"additional","affiliation":[{"name":"Computing, Environment and Life Sciences, Argonne National Laboratory , 9700 S Cass Ave, Lemont, IL 60439 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6567-0564","authenticated-orcid":false,"given":"Fangfang","family":"Xia","sequence":"additional","affiliation":[{"name":"Computing, Environment and Life Sciences, Argonne National Laboratory , 9700 S Cass Ave, Lemont, IL 60439 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maulik","family":"Shukla","sequence":"additional","affiliation":[{"name":"Computing, Environment and Life Sciences, Argonne National Laboratory , 9700 S Cass Ave, Lemont, IL 60439 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Priyanka","family":"Vasanthakumari","sequence":"additional","affiliation":[{"name":"Computing, Environment and Life Sciences, Argonne National Laboratory , 9700 S Cass Ave, Lemont, IL 60439 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James H","family":"Doroshow","sequence":"additional","affiliation":[{"name":"Developmental Therapeutics Branch, National Cancer Institute , 31 Center Dr, Bethesda, MD 20892 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4268-4020","authenticated-orcid":false,"given":"Rick L","family":"Stevens","sequence":"additional","affiliation":[{"name":"Computing, Environment and Life Sciences, Argonne National Laboratory , 9700 S Cass Ave, Lemont, IL 60439 ,","place":["United States"]},{"name":"Department of Computer Science, The University of Chicago , 5730 S Ellis Ave, Chicago, IL 60637 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"2025040311313384600_ref1","doi-asserted-by":"publisher","first-page":"2785","DOI":"10.1002\/cncr.31551","article-title":"Annual report to the nation on the status of cancer, part I: National cancer statistics","volume":"124","author":"Cronin","year":"2018","journal-title":"Cancer"},{"key":"2025040311313384600_ref2","doi-asserted-by":"publisher","first-page":"3029","DOI":"10.1002\/cncr.33587","article-title":"The ever-increasing importance of cancer as a leading cause of premature death worldwide","volume":"127","author":"Bray","year":"2021","journal-title":"Cancer"},{"key":"2025040311313384600_ref3","doi-asserted-by":"publisher","first-page":"i501","DOI":"10.1093\/bioinformatics\/btz318","article-title":"MOLI: Multi-omics late integration with deep neural networks for drug response prediction","volume":"35","author":"Sharifi-Noghabi","year":"2019","journal-title":"Bioinformatics"},{"key":"2025040311313384600_ref4","doi-asserted-by":"publisher","first-page":"1086097","DOI":"10.3389\/fmed.2023.1086097","article-title":"Deep learning methods for drug response prediction in cancer: Predominant and emerging trends","volume":"10","author":"Partin","year":"2023","journal-title":"Front Med"},{"key":"2025040311313384600_ref5","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine learning"},{"key":"2025040311313384600_ref6","doi-asserted-by":"publisher","first-page":"349","DOI":"10.4310\/SII.2009.v2.n3.a8","article-title":"Multi-class adaboost","volume":"2","author":"Hastie","year":"2009","journal-title":"Statistics and its Interface"},{"key":"2025040311313384600_ref7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4310\/SII.2009.v2.n3.a8","article-title":"Drug-induced cell viability prediction from LINCS-L1000 through WRFEN-XGBoost algorithm","volume":"22","author":"Lu","year":"2021","journal-title":"BMC bioinformatics"},{"key":"2025040311313384600_ref8","article-title":"Lightgbm: A highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"2025040311313384600_ref9","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.1109\/72.788646","article-title":"Support vector machines for histogram-based image classification","volume":"10","author":"Chapelle","year":"1999","journal-title":"IEEE Trans Neural Netw"},{"key":"2025040311313384600_ref10","doi-asserted-by":"publisher","first-page":"2347","DOI":"10.1021\/ci500152b","article-title":"Integrative and personalized QSAR analysis in cancer by kernelized Bayesian matrix factorization","volume":"54","author":"Ammad-Ud-Din","year":"2014","journal-title":"J Chem Inf Model"},{"key":"2025040311313384600_ref11","first-page":"92","volume-title":"Proceedings of the Conformal and Probabilistic Prediction with Applications","author":"Hern\u00e1ndez-Hern\u00e1ndez","year":"2022"},{"key":"2025040311313384600_ref12","doi-asserted-by":"publisher","first-page":"e61318","DOI":"10.1371\/journal.pone.0061318","article-title":"Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties","volume":"8","author":"Menden","year":"2013","journal-title":"PloS One"},{"key":"2025040311313384600_ref13","doi-asserted-by":"publisher","first-page":"6276","DOI":"10.3390\/ijms20246276","article-title":"A deep learning model for cell growth inhibition IC50 prediction and its application for gastric cancer patients","volume":"20","author":"Joo","year":"2019","journal-title":"Int J Mol Sci"},{"key":"2025040311313384600_ref14","doi-asserted-by":"publisher","first-page":"i911","DOI":"10.1093\/bioinformatics\/btaa822","article-title":"DeepCDR: A hybrid graph convolutional network for predicting cancer drug response","volume":"36","author":"Liu","year":"2020","journal-title":"Bioinformatics"},{"key":"2025040311313384600_ref15","doi-asserted-by":"publisher","first-page":"11325","DOI":"10.1038\/s41598-021-90923-y","article-title":"Converting tabular data into images for deep learning with convolutional neural networks","volume":"11","author":"Zhu","year":"2021","journal-title":"Sci Rep"},{"key":"2025040311313384600_ref16","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1109\/TCBB.2021.3060430","article-title":"Graph convolutional networks for drug response prediction","volume":"19","author":"Nguyen","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2025040311313384600_ref17","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1109\/TCBB.2022.3206888","article-title":"Graph transformer for drug response prediction","volume":"20","author":"Chu","year":"2022","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2025040311313384600_ref18","first-page":"1811.06802","article-title":"PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks","author":"Oskooei","year":"2018","journal-title":"arXiv preprint arXiv"},{"key":"2025040311313384600_ref19","first-page":"660","volume-title":"Proceedings of the Machine Learning for Healthcare Conference","author":"Tao","year":"2020"},{"key":"2025040311313384600_ref20","doi-asserted-by":"publisher","first-page":"bbac100","DOI":"10.1093\/bib\/bbac100","article-title":"DeepTTA: A transformer-based model for predicting cancer drug response","volume":"23","author":"Jiang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025040311313384600_ref21","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1038\/nmeth.2259","article-title":"Flaws in evaluation schemes for pair-input computational predictions","volume":"9","author":"Park","year":"2012","journal-title":"Nat Methods"},{"key":"2025040311313384600_ref22","doi-asserted-by":"publisher","first-page":"110045","DOI":"10.1016\/j.celrep.2021.110045","article-title":"Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning","volume":"37","author":"Narykov","year":"2021","journal-title":"Cell Rep"},{"key":"2025040311313384600_ref23","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1007\/BF02471624","article-title":"Chemosensitivity tests in colorectal cancer patients","volume":"19","author":"Yanagawa","year":"1989","journal-title":"Jpn J Surg"},{"key":"2025040311313384600_ref24","doi-asserted-by":"publisher","first-page":"18040","DOI":"10.1038\/s41598-020-74921-0","article-title":"Ensemble transfer learning for the prediction of anti-cancer drug response","volume":"10","author":"Zhu","year":"2020","journal-title":"Sci Rep"},{"key":"2025040311313384600_ref25","doi-asserted-by":"publisher","first-page":"1504","DOI":"10.1021\/ci700052x","article-title":"Comparison of topological, shape, and docking methods in virtual screening","volume":"47","author":"McGaughey","year":"2007","journal-title":"J Chem Inf Model"},{"key":"2025040311313384600_ref26","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.3390\/genes11091070","article-title":"Enhanced co-expression extrapolation (COXEN) gene selection method for building anti-cancer drug response prediction models","volume":"11","author":"Zhu","year":"2020","journal-title":"Genes"},{"key":"2025040311313384600_ref27","doi-asserted-by":"publisher","first-page":"1740","DOI":"10.1038\/s41467-021-21997-5","article-title":"Deep generative neural network for accurate drug response imputation","volume":"12","author":"Jia","year":"2021","journal-title":"Nat Commun"},{"key":"2025040311313384600_ref28","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1136\/oem.2002.001115","article-title":"Regression modelling and other methods to control confounding","volume":"62","author":"McNamee","year":"2005","journal-title":"Occup Environ Med"},{"key":"2025040311313384600_ref29","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1038\/s42256-022-00541-0","article-title":"A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening","volume":"4","author":"He","year":"2022","journal-title":"Nature Machine Intelligence"},{"key":"2025040311313384600_ref30","first-page":"58","volume-title":"Proceedings of the International Conference on Artificial Neural Networks","author":"Guo","year":"2024"},{"key":"2025040311313384600_ref31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-59656-2","article-title":"Assessment of modelling strategies for drug response prediction in cell lines and xenografts","volume":"10","author":"Kurilov","year":"2020","journal-title":"Sci Rep"},{"key":"2025040311313384600_ref32","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1016\/j.cell.2013.08.003","article-title":"An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules","volume":"154","author":"Basu","year":"2013","journal-title":"Cell"},{"key":"2025040311313384600_ref33","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":"2025040311313384600_ref34","author":"Chemoinformatics"},{"key":"2025040311313384600_ref35","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules","volume":"28","author":"Weininger","year":"1988","journal-title":"J Chem Inf Comput Sci"},{"key":"2025040311313384600_ref36","doi-asserted-by":"publisher","first-page":"D1102","DOI":"10.1093\/nar\/gky1033","article-title":"PubChem 2019 update: Improved access to chemical data","volume":"47","author":"Kim","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2025040311313384600_ref37","first-page":"121","volume-title":"Proceedings of the Proceedings of the SC'23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis","author":"Narykov","year":"2023"},{"key":"2025040311313384600_ref38","doi-asserted-by":"publisher","first-page":"110415","DOI":"10.1016\/j.asoc.2023.110415","article-title":"A broad review on class imbalance learning techniques","volume":"143","author":"Rezvani","year":"2023","journal-title":"Appl Soft Comput"},{"key":"2025040311313384600_ref39","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","article-title":"Learning from class-imbalanced data: Review of methods and applications","volume":"73","author":"Haixiang","year":"2017","journal-title":"Expert systems with applications"},{"key":"2025040311313384600_ref40","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J Artif Intell Res"},{"key":"2025040311313384600_ref41","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1007\/978-3-642-40669-0_33","volume-title":"Proceedings of the Portuguese conference on artificial intelligence","author":"Torgo","year":"2013"},{"key":"2025040311313384600_ref42","first-page":"452","volume-title":"Proceedings of the Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Gold Coast, QLD, Australia, April 14\u201317, 2013, Revised Selected Papers 17","author":"Cao","year":"2013"},{"key":"2025040311313384600_ref43","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1109\/PEDSTC.2016.7556909","volume-title":"Proceedings of the 2016 7th Power Electronics and Drive Systems Technologies Conference (PEDSTC)","author":"Abarzadeh","year":"2016"},{"key":"2025040311313384600_ref44","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/s12293-018-0267-4","article-title":"Class-specific cost-sensitive boosting weighted ELM for class imbalance learning","volume":"11","author":"Raghuwanshi","year":"2019","journal-title":"Memetic Computing"},{"key":"2025040311313384600_ref45","first-page":"257","volume-title":"Proceedings of the Proceedings 2001 IEEE international conference on data mining","author":"Joshi","year":"2001"},{"key":"2025040311313384600_ref46","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/FSKD.2009.608","volume-title":"Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery","author":"Song","year":"2009"},{"key":"2025040311313384600_ref47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2641758","article-title":"Probabilistic reframing for cost-sensitive regression","volume":"8","author":"Hern\u00b4 Ndez-Orallo","year":"2014","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"key":"2025040311313384600_ref48","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1007\/3-540-44719-9_22","volume-title":"Proceedings of the International Conference on Evolutionary Multi-Criterion Optimization","author":"Teich","year":"2001"},{"key":"2025040311313384600_ref49","first-page":"1412.6980","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv preprint arXiv"},{"key":"2025040311313384600_ref50","doi-asserted-by":"publisher","first-page":"347","DOI":"10.3758\/BF03207805","article-title":"Statistical power for the two-factor repeated measures ANOVA","volume":"32","author":"Potvin","year":"2000","journal-title":"Behav Res Methods Instrum Comput"},{"key":"2025040311313384600_ref51","doi-asserted-by":"publisher","first-page":"544","DOI":"10.3758\/BF03207805","article-title":"The greenhouse-geisser correction","volume":"1","author":"Abdi","year":"2010","journal-title":"Encyclopedia of research design"},{"key":"2025040311313384600_ref52","volume-title":"Proceedings of the NeurIPS Learning Meaningful Representation of Life Workshop","author":"Huang","year":"2019"},{"key":"2025040311313384600_ref53","doi-asserted-by":"publisher","first-page":"5441","DOI":"10.1039\/C8SC00148K","article-title":"Large-scale comparison of machine learning methods for drug target prediction on ChEMBL","volume":"9","author":"Mayr","year":"2018","journal-title":"Chem Sci"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/2\/bbaf134\/62852862\/bbaf134.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/2\/bbaf134\/62852862\/bbaf134.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T07:56:25Z","timestamp":1743666985000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf134\/8104856"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":53,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,3,4]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf134","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.03.14.585074","asserted-by":"object"}]},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,3]]},"published":{"date-parts":[[2025,3]]},"article-number":"bbaf134"}}