{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T21:52:45Z","timestamp":1768686765134,"version":"3.49.0"},"reference-count":13,"publisher":"Oxford University Press (OUP)","issue":"19","funder":[{"DOI":"10.13039\/100006379","name":"Office of Research and Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006379","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000139","name":"United States Environmental Protection Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000139","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Generalized Read-Across (GenRA) is a data-driven approach to estimate physico-chemical, biological or eco-toxicological properties of chemicals by inference from analogues. GenRA attempts to mimic a human expert\u2019s manual read-across reasoning for filling data gaps about new chemicals from known chemicals with an interpretable and automated approach based on nearest-neighbors. A key objective of GenRA is to systematically explore different choices of input data selection and neighborhood definition to objectively evaluate predictive performance of automated read-across estimates of chemical properties.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We have implemented genra-py as a python package that can be freely used for chemical safety analysis and risk assessment applications. Automated read-across prediction in genra-py conforms to the scikit-learn machine learning library's estimator design pattern, making it easy to use and integrate in computational pipelines. We demonstrate the data-driven application of genra-py to address two key human health risk assessment problems namely: hazard identification and point of departure estimation.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The package is available from github.com\/i-shah\/genra-py.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab210","type":"journal-article","created":{"date-parts":[[2021,3,26]],"date-time":"2021-03-26T02:09:37Z","timestamp":1616724577000},"page":"3380-3381","source":"Crossref","is-referenced-by-count":19,"title":["Generalized Read-Across prediction using genra-py"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0808-0140","authenticated-orcid":false,"given":"Imran","family":"Shah","sequence":"first","affiliation":[{"name":"Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency , Research Triangle Park, NC 27709, USA"}]},{"given":"Tia","family":"Tate","sequence":"additional","affiliation":[{"name":"Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency , Research Triangle Park, NC 27709, USA"}]},{"given":"Grace","family":"Patlewicz","sequence":"additional","affiliation":[{"name":"Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency , Research Triangle Park, NC 27709, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,3,27]]},"reference":[{"key":"2023051608260723800_btab210-B1","first-page":"1","article-title":"Generalized Read-Across (GenRA): a workflow implemented into the EPA CompTox Chemicals Dashboard","volume":"36","author":"Helman","year":"2019","journal-title":"ALTEX"},{"key":"2023051608260723800_btab210-B2","doi-asserted-by":"crossref","first-page":"100097","DOI":"10.1016\/j.comtox.2019.100097","article-title":"Transitioning the generalised read-across approach (GenRA) to quantitative predictions: a case study using acute oral toxicity data","volume":"12","author":"Helman","year":"2019","journal-title":"Comput. Toxicol"},{"key":"2023051608260723800_btab210-B3","author":"Landrum","year":"2015"},{"key":"2023051608260723800_btab210-B4","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1021\/tx400110f","article-title":"Integrative chemical\u2013biological read-across approach for chemical hazard classification","volume":"26","author":"Low","year":"2013","journal-title":"Chem. Res. Toxicol"},{"key":"2023051608260723800_btab210-B5","year":"2017"},{"key":"2023051608260723800_btab210-B6","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.comtox.2018.04.002","article-title":"Navigating through the minefield of read-across frameworks: a commentary perspective","volume":"6","author":"Patlewicz","year":"2018","journal-title":"Comput. Toxicol"},{"key":"2023051608260723800_btab210-B7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.comtox.2017.05.003","article-title":"Navigating through the minefield of read-across tools: a review of in silico tools for grouping","volume":"3","author":"Patlewicz","year":"2017","journal-title":"Comput. Toxicol"},{"key":"2023051608260723800_btab210-B8","first-page":"2825","article-title":"Scikit-learn: machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. 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Pharmacol"},{"key":"2023051608260723800_btab210-B11","year":"1996"},{"key":"2023051608260723800_btab210-B12","year":"2018"},{"key":"2023051608260723800_btab210-B13","year":"2018"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btab210\/37038247\/btab210.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/19\/3380\/50338119\/btab210.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/19\/3380\/50338119\/btab210.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T08:38:18Z","timestamp":1684226298000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/37\/19\/3380\/6194561"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,3,27]]},"references-count":13,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2021,10,11]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btab210","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,10,1]]},"published":{"date-parts":[[2021,3,27]]}}}