{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T20:24:06Z","timestamp":1776025446835,"version":"3.50.1"},"reference-count":81,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["K99GM135488"],"award-info":[{"award-number":["K99GM135488"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Internal validation is the most popular evaluation strategy used for drug\u2013target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models cannot be comprehensively evaluated to provide insight into their general performance on a variety of use-cases (e.g. permutations of different levels of connectiveness and categories in drug and target space, as well as validations based on different data sources). In this work, we introduce a benchmark, BETA, that aims to address this gap by (i) providing an extensive multipartite network consisting of 0.97 million biomedical concepts and 8.5 million associations, in addition to 62 million drug\u2013drug and protein\u2013protein similarities and (ii) presenting evaluation strategies that reflect seven cases (i.e. general, screening with different connectivity, target and drug screening based on categories, searching for specific drugs and targets and drug repurposing for specific diseases), a total of seven Tests (consisting of 344 Tasks in total) across multiple sampling and validation strategies. Six state-of-the-art methods covering two broad input data types (chemical structure- and gene sequence-based and network-based) were tested across all the developed Tasks. The best-worst performing cases have been analyzed to demonstrate the ability of the proposed benchmark to identify limitations of the tested methods for running over the benchmark tasks. The results highlight BETA as a benchmark in the selection of computational strategies for drug repurposing and target discovery.<\/jats:p>","DOI":"10.1093\/bib\/bbac199","type":"journal-article","created":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T03:44:37Z","timestamp":1651635877000},"source":"Crossref","is-referenced-by-count":17,"title":["BETA: a comprehensive benchmark for computational drug\u2013target prediction"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0066-9524","authenticated-orcid":false,"given":"Nansu","family":"Zong","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence and Informatics Research , Mayo Clinic, Rochester , MN"}]},{"given":"Ning","family":"Li","sequence":"additional","affiliation":[{"name":"Center for Structure Biology, Center for Cancer Research, National Cancer Institute , Frederick , MD"}]},{"given":"Andrew","family":"Wen","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Informatics Research , Mayo Clinic, Rochester , MN"}]},{"given":"Victoria","family":"Ngo","sequence":"additional","affiliation":[{"name":"Betty Irene Moore School of Nursing, University of California Davis Health , Sacramento , CA"},{"name":"Stanford Health Policy, Stanford School of Medicine and Freeman Spogli Institute for International Studies , Palo Alto , CA"}]},{"given":"Yue","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Informatics Research , Mayo Clinic, Rochester , MN"}]},{"given":"Ming","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Informatics Research , Mayo Clinic, Rochester , MN"}]},{"given":"Shaika","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Informatics Research , Mayo Clinic, Rochester , MN"}]},{"given":"Chao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, Auburn University , Auburn , AL"}]},{"given":"Sunyang","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Informatics Research , Mayo Clinic, Rochester , MN"}]},{"given":"Richard","family":"Weinshilboum","sequence":"additional","affiliation":[{"name":"Department of Molecular Pharmacology and Experimental Therapeutics , Mayo Clinic, Rochester , MN"}]},{"given":"Guoqian","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Informatics Research , Mayo Clinic, Rochester , MN"}]},{"given":"Lawrence","family":"Hunter","sequence":"additional","affiliation":[{"name":"Department of Pharmacology, University of Colorado Denver , Aurora , CO"}]},{"given":"Hongfang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Informatics Research , Mayo Clinic, Rochester , MN"}]}],"member":"286","published-online":{"date-parts":[[2022,6,2]]},"reference":[{"key":"2022071906085298300_ref1","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1038\/nrd.2016.230","article-title":"A comprehensive map of molecular drug targets","volume":"16","author":"Santos","year":"2017","journal-title":"Nat Rev Drug Discov"},{"key":"2022071906085298300_ref2","doi-asserted-by":"crossref","first-page":"i18","DOI":"10.1093\/bioinformatics\/btw244","article-title":"DrugE-rank: improving drug\u2013target interaction prediction of new candidate drugs or targets by ensemble learning to 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