{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:08:31Z","timestamp":1790726911455,"version":"4.1.0"},"reference-count":29,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007472","name":"University of Kentucky","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007472","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["AG084180"],"award-info":[{"award-number":["AG084180"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["DK135111"],"award-info":[{"award-number":["DK135111"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["GM156679"],"award-info":[{"award-number":["GM156679"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["slas-discovery.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["SLAS Discovery"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.slasd.2026.100340","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T09:13:40Z","timestamp":1789722820000},"page":"100340","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Hit selection using SSMD-based machine learning performance metrics in high-throughput screening assays"],"prefix":"10.1016","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2486-7931","authenticated-orcid":false,"given":"Xiaohua Douglas","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.slasd.2026.100340_bib0001","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1038\/nrg3899","article-title":"High-throughput functional genomics using CRISPR-Cas9","volume":"16","author":"Shalem","year":"2015","journal-title":"Nat Rev Genet"},{"key":"10.1016\/j.slasd.2026.100340_bib0002","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1038\/nrd1343","article-title":"Small-molecule inhibitors of protein-protein interactions: progressing towards the dream","volume":"3","author":"Arkin","year":"2004","journal-title":"Nat Rev Drug Discov"},{"key":"10.1016\/j.slasd.2026.100340_bib0003","series-title":"Optimal High-Throughput Screening: Practical Experimental Design and Data Analysis for Genome-Scale RNAi Research","author":"Zhang","year":"2011"},{"key":"10.1016\/j.slasd.2026.100340_bib0004","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1177\/1087057107300645","article-title":"A new method with flexible and balanced control of false negatives and false positives for hit selection in RNA interference high-throughput screening assays","volume":"12","author":"Zhang","year":"2007","journal-title":"J Biomol Screen"},{"key":"10.1016\/j.slasd.2026.100340_bib0005","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1177\/1087057107300646","article-title":"The use of strictly standardized mean difference for hit selection in primary RNA interference high-throughput screening experiments","volume":"12","author":"Zhang","year":"2007","journal-title":"J Biomol Screen"},{"key":"10.1016\/j.slasd.2026.100340_bib0006","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1016\/j.ygeno.2006.12.014","article-title":"A pair of new statistical parameters for quality control in RNA interference high-throughput screening assays","volume":"89","author":"Zhang","year":"2007","journal-title":"Genomics"},{"key":"10.1016\/j.slasd.2026.100340_bib0007","doi-asserted-by":"crossref","first-page":"2982","DOI":"10.1016\/j.molcel.2022.06.036","article-title":"Systematic exploration of dynamic splicing networks reveals conserved multistage regulators of neurogenesis","volume":"82","author":"Han","year":"2022","journal-title":"Mol Cell"},{"key":"10.1016\/j.slasd.2026.100340_bib0008","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.ady5599","article-title":"Inclusion of JNK-independent drugs within multiagent chemotherapy improves response in relapsed high-risk neuroblastoma","volume":"11","author":"Han","year":"2025","journal-title":"Sci Adv"},{"key":"10.1016\/j.slasd.2026.100340_bib0009","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1186\/s13059-022-02729-4","article-title":"ZetaSuite: computational analysis of two-dimensional high-throughput data from multi-target screens and single-cell transcriptomics","volume":"23","author":"Hao","year":"2022","journal-title":"Genome Biol"},{"key":"10.1016\/j.slasd.2026.100340_bib0010","doi-asserted-by":"crossref","first-page":"2825","DOI":"10.1016\/j.stemcr.2021.09.020","article-title":"A new platform for high-throughput therapy testing on iPSC-derived lung progenitor cells from cystic fibrosis patients","volume":"16","author":"Jiang","year":"2021","journal-title":"Stem Cell Rep"},{"key":"10.1016\/j.slasd.2026.100340_bib0011","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.slasd.2023.02.003","article-title":"Mathematical relationships between control group variability and assay quality metrics","volume":"28","author":"Lim","year":"2023","journal-title":"SLAS Discov"},{"key":"10.1016\/j.slasd.2026.100340_bib0012","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.adx4562","article-title":"Small molecule OPA1 inhibitors amplify cytochrome c release and reverse cancer cells resistance to Bcl-2 inhibitors","volume":"11","author":"Pellattiero","year":"2025","journal-title":"Sci Adv"},{"key":"10.1016\/j.slasd.2026.100340_bib0013","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1016\/j.cmet.2021.02.005","article-title":"CRISPR screens in physiologic medium reveal conditionally essential genes in human cells","volume":"33","author":"Rossiter","year":"2021","journal-title":"Cell Metab"},{"key":"10.1016\/j.slasd.2026.100340_bib0014","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1038\/s41551-024-01278-4","article-title":"Arrayed CRISPR libraries for the genome-wide activation, deletion and silencing of human protein-coding genes","volume":"9","author":"Yin","year":"2025","journal-title":"Nat Biomed Eng"},{"key":"10.1016\/j.slasd.2026.100340_bib0015","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/1756-0500-1-33","article-title":"Genome-wide screens for effective siRNAs through assessing the size of siRNA effects","volume":"1","author":"Zhang","year":"2008","journal-title":"BMC Res Notes"},{"key":"10.1016\/j.slasd.2026.100340_bib0016","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1177\/1087057108317062","article-title":"Novel analytic criteria and effective plate designs for quality control in genome-scale RNAi screens","volume":"13","author":"Zhang","year":"2008","journal-title":"J Biomol Screen"},{"key":"10.1016\/j.slasd.2026.100340_bib0017","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1177\/1087057110381783","article-title":"An effective method for controlling false discovery and false nondiscovery rates in genome-scale RNAi screens","volume":"15","author":"Zhang","year":"2010","journal-title":"J Biomol Screen"},{"key":"10.1016\/j.slasd.2026.100340_bib0018","doi-asserted-by":"crossref","first-page":"2775","DOI":"10.1093\/bioinformatics\/btr474","article-title":"cSSMD: assessing collective activity for addressing off-target effects in genome-scale RNA interference screens","volume":"27","author":"Zhang","year":"2011","journal-title":"Bioinformatics"},{"key":"10.1016\/j.slasd.2026.100340_bib0019","doi-asserted-by":"crossref","first-page":"5299","DOI":"10.1093\/bioinformatics\/btaa1049","article-title":"Issues of Z-factor and an approach to avoid them for quality control in high-throughput screening studies","volume":"36","author":"Zhang","year":"2021","journal-title":"Bioinformatics"},{"key":"10.1016\/j.slasd.2026.100340_bib0020","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.chom.2008.10.004","article-title":"Genome-Scale RNAi screen for host factors required for HIV replication","volume":"4","author":"Zhou","year":"2008","journal-title":"Cell Host Microbe"},{"key":"10.1016\/j.slasd.2026.100340_bib0021","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1038\/nature13166","article-title":"High-throughput screening of a CRISPR\/Cas9 library for functional genomics in human cells","volume":"509","author":"Zhou","year":"2014","journal-title":"Nature"},{"key":"10.1016\/j.slasd.2026.100340_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.slasd.2025.100269","article-title":"Integrating AUROC and SSMD for quality control in high-throughput screening assays","volume":"36","author":"Zhang","year":"2025","journal-title":"SLAS Discov"},{"key":"10.1016\/j.slasd.2026.100340_bib0023","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.cell.2020.01.021","article-title":"A deep learning approach to antibiotic discovery","volume":"180","author":"Stokes","year":"2020","journal-title":"Cell"},{"key":"10.1016\/j.slasd.2026.100340_bib0024","doi-asserted-by":"crossref","first-page":"299","DOI":"10.2217\/14622416.7.3.299","article-title":"Robust statistical methods for hit selection in RNA interference high-throughput screening experiments","volume":"7","author":"Zhang","year":"2006","journal-title":"Pharmacogenomics"},{"key":"10.1016\/j.slasd.2026.100340_bib0025","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The meaning and use of the area under a receiver operating characteristic (ROC) curve","volume":"143","author":"Hanley","year":"1982","journal-title":"Radiology"},{"key":"10.1016\/j.slasd.2026.100340_bib0026","doi-asserted-by":"crossref","first-page":"269","DOI":"10.14778\/3157794.3157797","article-title":"Snorkel: rapid training data creation with weak supervision","volume":"11","author":"Ratner","year":"2017","journal-title":"Proc VLDB Endow"},{"key":"10.1016\/j.slasd.2026.100340_bib0027","series-title":"Proceedings of the 32nd International Conference on Machine Learning","first-page":"125","article-title":"Learning from corrupted binary labels via class-probability estimation","volume":"37","author":"Menon","year":"2015"},{"key":"10.1016\/j.slasd.2026.100340_bib0028","series-title":"Proceedings of the 34th International Conference on Machine Learning - Volume 70, JMLR.org: Sydney","first-page":"2998","article-title":"Semi-supervised classification based on classification from positive and unlabeled data","author":"Sakai","year":"2017"},{"key":"10.1016\/j.slasd.2026.100340_bib0029","series-title":"International Conference on Machine Learning","article-title":"The case against accuracy estimation for comparing induction algorithms","author":"Provost","year":"1998"}],"container-title":["SLAS Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2472555226000468?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2472555226000468?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T23:46:33Z","timestamp":1790725593000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2472555226000468"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":29,"alternative-id":["S2472555226000468"],"URL":"https:\/\/doi.org\/10.1016\/j.slasd.2026.100340","relation":{},"ISSN":["2472-5552"],"issn-type":[{"value":"2472-5552","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Hit selection using SSMD-based machine learning performance metrics in high-throughput screening assays","name":"articletitle","label":"Article Title"},{"value":"SLAS Discovery","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.slasd.2026.100340","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author. Published by Elsevier Inc. on behalf of Society for Laboratory Automation and Screening.","name":"copyright","label":"Copyright"}],"article-number":"100340"}}