{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T22:05:24Z","timestamp":1775081124870,"version":"3.50.1"},"reference-count":45,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T00:00:00Z","timestamp":1662508800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072329"],"award-info":[{"award-number":["62072329"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071278"],"award-info":[{"award-number":["62071278"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Drug-induced toxicity damages the health and is one of the key factors causing drug withdrawal from the market. It is of great significance to identify drug-induced target-organ toxicity, especially the detailed pathological findings, which are crucial for toxicity assessment, in the early stage of drug development process. A large variety of studies have devoted to identify drug toxicity. However, most of them are limited to single organ or only binary toxicity. Here we proposed a novel multi-label learning model named Att-RethinkNet, for predicting drug-induced pathological findings targeted on liver and kidney based on toxicogenomics data. The Att-RethinkNet is equipped with a memory structure and can effectively use the label association information. Besides, attention mechanism is embedded to focus on the important features and obtain better feature presentation. Our Att-RethinkNet is applicable in multiple organs and takes account the compound type, dose, and administration time, so it is more comprehensive and generalized. And more importantly, it predicts multiple pathological findings at the same time, instead of predicting each pathology separately as the previous model did. To demonstrate the effectiveness of the proposed model, we compared the proposed method with a series of state-of-the-arts methods. Our model shows competitive performance and can predict potential hepatotoxicity and nephrotoxicity in a more accurate and reliable way. The implementation of the proposed method is available at<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/RanSuLab\/Drug-Toxicity-Prediction-MultiLabel\" xlink:type=\"simple\">https:\/\/github.com\/RanSuLab\/Drug-Toxicity-Prediction-MultiLabel<\/jats:ext-link>.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1010402","type":"journal-article","created":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T17:31:35Z","timestamp":1662571895000},"page":"e1010402","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":29,"title":["A multi-label learning model for predicting drug-induced pathology in multi-organ based on toxicogenomics data"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5922-0364","authenticated-orcid":true,"given":"Ran","family":"Su","sequence":"first","affiliation":[]},{"given":"Haitang","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Leyi","family":"Wei","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2460-4381","authenticated-orcid":true,"given":"Siqi","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-1142","authenticated-orcid":true,"given":"Quan","family":"Zou","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"issue":"3","key":"pcbi.1010402.ref001","first-page":"170","article-title":"Drugs, devices, and the FDA: part 1: an overview of approval processes for drugs","volume":"1","author":"Norman Van","year":"2016","journal-title":"JACC: Basic to Translational Science"},{"issue":"1","key":"pcbi.1010402.ref002","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.healthpol.2010.12.002","article-title":"The cost of drug development:a systematic review","volume":"100","author":"S Morgan","year":"2011","journal-title":"Health policy"},{"issue":"D1","key":"pcbi.1010402.ref003","doi-asserted-by":"crossref","first-page":"D1080","DOI":"10.1093\/nar\/gkv1192","article-title":"WITHDRAWN\u2013resource for withdrawn and discontinued drugs","volume":"44","author":"VB Siramshetty","year":"2016","journal-title":"Nucleic acids research"},{"issue":"1","key":"pcbi.1010402.ref004","first-page":"1","article-title":"Repeated dose multi-drug testing using a microfluidic chip-based coculture of human liver and kidney proximal tubules equivalents","volume":"10","author":"NI Lin","year":"2020","journal-title":"Scientific reports"},{"issue":"2","key":"pcbi.1010402.ref005","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.taap.2009.11.019","article-title":"Metabolomics approaches for discovering biomarkers of drug-induced hepatotoxicity and nephrotoxicity","volume":"243","author":"RD Beger","year":"2010","journal-title":"Toxicology and applied pharmacology"},{"issue":"4","key":"pcbi.1010402.ref006","first-page":"42","article-title":"Toxicogenomics","volume":"2","author":"S Amala","year":"2010","journal-title":"Journal of Bioinformatics and Sequence Analysis"},{"issue":"4","key":"pcbi.1010402.ref007","first-page":"605","article-title":"Approaches and perspectives to toxicogenetics and toxicogenomics","volume":"62","author":"F Ancizar-Aristiz\u00e1bal","year":"2014","journal-title":"Revista de la Facultad de Medicina"},{"key":"pcbi.1010402.ref008","unstructured":"National Research Council. 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