{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T02:34:34Z","timestamp":1769826874271,"version":"3.49.0"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute Of General Medical Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01GM122084"],"award-info":[{"award-number":["R01GM122084"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CCF 2007903"],"award-info":[{"award-number":["CCF 2007903"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Anti-cancer drug sensitivity prediction using deep learning models for individual cell line is a significant challenge in personalized medicine. Recently developed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) CNN (Convolutional Neural Network)-based models have shown promising results in improving drug sensitivity prediction. The primary idea behind REFINED-CNN is representing high dimensional vectors as compact images with spatial correlations that can benefit from CNN architectures. However, the mapping from a high dimensional vector to a compact 2D image depends on the a priori choice of the distance metric and projection scheme with limited empirical procedures guiding these choices.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this article, we consider an ensemble of REFINED-CNN built under different choices of distance metrics and\/or projection schemes that can improve upon a single projection based REFINED-CNN model. Results, illustrated using NCI60 and NCI-ALMANAC databases, demonstrate that the ensemble approaches can provide significant improvement in prediction performance as compared to individual models. We also develop the theoretical framework for combining different distance metrics to arrive at a single 2D mapping. Results demonstrated that distance-averaged REFINED-CNN produced comparable performance as obtained from stacking REFINED-CNN ensemble but with significantly lower computational cost.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code, scripts, and data used in the paper have been deposited in GitHub (https:\/\/github.com\/omidbazgirTTU\/IntegratedREFINED).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab336","type":"journal-article","created":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T19:18:09Z","timestamp":1619810289000},"page":"i42-i50","source":"Crossref","is-referenced-by-count":19,"title":["Investigation of REFINED CNN ensemble learning for anti-cancer drug sensitivity prediction"],"prefix":"10.1093","volume":"37","author":[{"given":"Omid","family":"Bazgir","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas Tech University , Lubbock, TX 79409, USA"}]},{"given":"Souparno","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, University of Nebraska-Lincoln , Lincoln, NE 68583, USA"}]},{"given":"Ranadip","family":"Pal","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas Tech University , Lubbock, TX 79409, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,7,12]]},"reference":[{"key":"2023062410295036100_btab336-B1","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1093\/pan\/mps039","article-title":"Bayesian metric multidimensional scaling","volume":"21","author":"Bakker","year":"2013","journal-title":"Political Anal"},{"key":"2023062410295036100_btab336-B2","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":"2023062410295036100_btab336-B3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-020-18197-y","article-title":"Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks","volume":"11","author":"Bazgir","year":"2020","journal-title":"Nat. Commun"},{"key":"2023062410295036100_btab336-B4","doi-asserted-by":"crossref","first-page":"3609","DOI":"10.1109\/JSEN.2020.3028362","article-title":"Active shooter detection in multiple-person scenario using rf based machine vision","volume":"21","author":"Bazgir","year":"2021","journal-title":"IEEE Sensors J"},{"key":"2023062410295036100_btab336-B5","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1162\/089976603321780317","article-title":"Laplacian eigenmaps for dimensionality reduction and data representation","volume":"15","author":"Belkin","year":"2003","journal-title":"Neural Comput"},{"key":"2023062410295036100_btab336-B6","first-page":"115","author":"Bergstra","year":"2013"},{"key":"2023062410295036100_btab336-B7","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1214\/12-AOAS610","article-title":"Spatially explicit models for inference about density in unmarked or partially marked populations","volume":"7","author":"Chandler","year":"2013","journal-title":"Ann. Appl. Stat"},{"key":"2023062410295036100_btab336-B8","first-page":"1","article-title":"Cancer drug response profile scan (CDRScan): a deep learning model that predicts drug effectiveness from cancer genomic signature","volume":"8","author":"Chang","year":"2018","journal-title":"Sci. Rep"},{"key":"2023062410295036100_btab336-B9","doi-asserted-by":"crossref","first-page":"2066","DOI":"10.1093\/bib\/bbz144","article-title":"Deep learning of pharmacogenomics resources: moving towards precision oncology","volume":"21","author":"Chiu","year":"2020","journal-title":"Brief. Bioinf"},{"key":"2023062410295036100_btab336-B10","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1038\/nbt.2877","article-title":"A community effort to assess and improve drug sensitivity prediction algorithms","volume":"32","author":"Costello","year":"2014","journal-title":"Nat. Biotechnol"},{"key":"2023062410295036100_btab336-B11","first-page":"155","author":"Drucker","year":"1997"},{"key":"2023062410295036100_btab336-B12","first-page":"1","author":"Efron","year":"1979"},{"key":"2023062410295036100_btab336-B13","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal"},{"key":"2023062410295036100_btab336-B14","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1038\/nature11005","article-title":"Systematic identification of genomic markers of drug sensitivity in cancer cells","volume":"483","author":"Garnett","year":"2012","journal-title":"Nature"},{"key":"2023062410295036100_btab336-B15","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/B978-0-323-35762-3.00057-3","volume-title":"Hematology","author":"Gerson","year":"2018","edition":"7th edn"},{"key":"2023062410295036100_btab336-B16","doi-asserted-by":"crossref","first-page":"i556","DOI":"10.1093\/bioinformatics\/btu464","article-title":"Drug susceptibility prediction against a panel of drugs using Kernelized Bayesian multitask learning","volume":"30","author":"G\u00f6nen","year":"2014","journal-title":"Bioinformatics"},{"key":"2023062410295036100_btab336-B17","first-page":"278","author":"Ho","year":"1995"},{"key":"2023062410295036100_btab336-B18","doi-asserted-by":"crossref","first-page":"3564","DOI":"10.1158\/0008-5472.CAN-17-0489","article-title":"The national cancer institute almanac: a comprehensive screening resource for the detection of anticancer drug pairs with enhanced therapeutic activity","volume":"77","author":"Holbeck","year":"2017","journal-title":"Cancer Res"},{"key":"2023062410295036100_btab336-B19","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"3d convolutional neural networks for human action recognition","volume":"35","author":"Ji","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"2023062410295036100_btab336-B20","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.3389\/fphar.2019.01526","article-title":"Deepmalaria: artificial intelligence driven discovery of potent antiplasmodials","volume":"10","author":"Keshavarzi Arshadi","year":"2019","journal-title":"Front. Pharmacol"},{"key":"2023062410295036100_btab336-B21","author":"Kondratyuk","year":"2020"},{"key":"2023062410295036100_btab336-B22","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1186\/s12859-019-2910-6","article-title":"Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network","volume":"20","author":"Liu","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2023062410295036100_btab336-B23","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1186\/s12859-018-2060-2","article-title":"Investigation of model stacking for drug sensitivity prediction","volume":"19","author":"Matlock","year":"2018","journal-title":"BMC Bioinformatics"},{"key":"2023062410295036100_btab336-B24","first-page":"922","author":"Maturana","year":"2015"},{"key":"2023062410295036100_btab336-B25","author":"Mostavi","year":"2020"},{"key":"2023062410295036100_btab336-B26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12920-020-0677-2","article-title":"Convolutional neural network models for cancer type prediction based on gene expression","volume":"13","author":"Mostavi","year":"2020","journal-title":"BMC Med. Genomics"},{"key":"2023062410295036100_btab336-B27","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1198\/016214501753208690","article-title":"Bayesian multidimensional scaling and choice of dimension","volume":"96","author":"Oh","year":"2001","journal-title":"J. Am. Stat. Assoc"},{"key":"2023062410295036100_btab336-B28","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1093\/bioinformatics\/btx806","article-title":"Deepsynergy: predicting anti-cancer drug synergy with deep learning","volume":"34","author":"Preuer","year":"2018","journal-title":"Bioinformatics"},{"key":"2023062410295036100_btab336-B29","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1146\/annurev-pharmtox-010919-023746","article-title":"Artificial intelligence in drug treatment","volume":"60","author":"Romm","year":"2020","journal-title":"Annu. Rev. Pharmacol. Toxicol"},{"key":"2023062410295036100_btab336-B30","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality reduction by locally linear embedding","volume":"290","author":"Roweis","year":"2000","journal-title":"Science"},{"key":"2023062410295036100_btab336-B31","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1038\/nrc1951","article-title":"The nci60 human tumour cell line anticancer drug screen","volume":"6","author":"Shoemaker","year":"2006","journal-title":"Nat. Rev. Cancer"},{"key":"2023062410295036100_btab336-B32","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.jkss.2015.01.003","article-title":"Bias corrections for random forest in regression using residual rotation","volume":"44","author":"Song","year":"2015","journal-title":"J. Korean Stat. Soc"},{"key":"2023062410295036100_btab336-B33","doi-asserted-by":"crossref","first-page":"i23","DOI":"10.1093\/bioinformatics\/btz370","article-title":"Learning a mixture of microbial networks using minorization\u2013maximization","volume":"35","author":"Tavakoli","year":"2019","journal-title":"Bioinformatics"},{"key":"2023062410295036100_btab336-B34","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","article-title":"A global geometric framework for nonlinear dimensionality reduction","volume":"290","author":"Tenenbaum","year":"2000","journal-title":"Science"},{"key":"2023062410295036100_btab336-B35","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1111\/1467-9868.00293","article-title":"Estimating the number of clusters in a data set via the gap statistic","volume":"63","author":"Tibshirani","year":"2001","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"key":"2023062410295036100_btab336-B36","doi-asserted-by":"crossref","first-page":"e101183","DOI":"10.1371\/journal.pone.0101183","article-title":"An ensemble based top performing approach for NCI-dream drug sensitivity prediction challenge","volume":"9","author":"Wan","year":"2014","journal-title":"PloS One"},{"key":"2023062410295036100_btab336-B37","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1186\/s12859-018-2509-3","article-title":"Predicting tumor cell line response to drug pairs with deep learning","volume":"19","author":"Xia","year":"2018","journal-title":"BMC Bioinformatics"},{"key":"2023062410295036100_btab336-B38","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1038\/s41540-020-0136-x","article-title":"Stratification and prediction of drug synergy based on target functional similarity","volume":"6","author":"Yang","year":"2020","journal-title":"NPJ Syst. Biol. Appl"},{"key":"2023062410295036100_btab336-B39","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1002\/jcc.21707","article-title":"Padel-descriptor: an open source software to calculate molecular descriptors and fingerprints","volume":"32","author":"Yap","year":"2011","journal-title":"J. Comput. Chem"},{"key":"2023062410295036100_btab336-B40","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1186\/s12864-019-5546-z","article-title":"Architectures and accuracy of artificial neural network for disease classification from omics data","volume":"20","author":"Yu","year":"2019","journal-title":"BMC Genomics"},{"key":"2023062410295036100_btab336-B41","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/Supplement_1\/i42\/50694117\/btab336.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/Supplement_1\/i42\/50694117\/btab336.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,25]],"date-time":"2023-06-25T00:22:25Z","timestamp":1687652545000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/37\/Supplement_1\/i42\/6319709"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,1]]},"references-count":41,"journal-issue":{"issue":"Supplement_1","published-print":{"date-parts":[[2021,8,4]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btab336","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,7,1]]},"published":{"date-parts":[[2021,7,1]]}}}