{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T03:24:58Z","timestamp":1772767498369,"version":"3.50.1"},"reference-count":42,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T00:00:00Z","timestamp":1757030400000},"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":["Grant No. 62222311"],"award-info":[{"award-number":["Grant No. 62222311"]}],"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 liver injury is a leading cause of high attrition rates for both candidate drugs and marketed medications. Previous in silico models may not effectively utilize biological drug property information and often lack robust model validation. In this study, we developed a graph convolutional network embedded with a biological graph learning (BioGL) module\u2014named BioGL-GCN(Biological Graph Learning-Graph Convolutional Network)\u2014for drug-induced liver injury prediction using toxicogenomic profiles. The BioGL module learned the optimal graph representations of gene interactions by utilizing the constructed protein-protein interaction network, which represents initial gene relationships, and gene frequency information obtained from gene enrichment analysis. Finally, the graph convolutional network was used to identify drug hepatotoxicity. Our method pays more attention to gene-gene relationships compared to previous approaches, thereby achieving more accurate predictive performance. We applied BioGL-GCN to predict DILI risk for active components in the integrated traditional Chinese medicine (ITCM) database and validated these predictions through hepatotoxicity experiments using a 3D primary human hepatocyte (PHH) model. The results showed that our model achieved a prediction accuracy of 79%, thus further validating the reliability of the constructed model.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1013423","type":"journal-article","created":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T17:42:27Z","timestamp":1757094147000},"page":"e1013423","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Drug-induced liver injury prediction based on graph convolutional networks and toxicogenomics"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2462-5853","authenticated-orcid":true,"given":"Tong","family":"Xiao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaimiao","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaimin","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengying","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"TingTing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihua","family":"Lei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjia","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuiping","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunhui","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5922-0364","authenticated-orcid":true,"given":"Ran","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,9,5]]},"reference":[{"issue":"7","key":"pcbi.1013423.ref001","doi-asserted-by":"crossref","first-page":"483","DOI":"10.2165\/00002018-200124070-00001","article-title":"Drug-induced liver disorders: implications for drug development and regulation","volume":"24","author":"N Kaplowitz","year":"2001","journal-title":"Drug Saf."},{"issue":"10","key":"pcbi.1013423.ref002","doi-asserted-by":"crossref","first-page":"3381","DOI":"10.1007\/s00204-020-02885-1","article-title":"Drug induced liver injury: an update","volume":"94","author":"M Garcia-Cortes","year":"2020","journal-title":"Arch Toxicol."},{"issue":"1","key":"pcbi.1013423.ref003","doi-asserted-by":"crossref","first-page":"4","DOI":"10.2174\/1574886314666191004092520","article-title":"Drug Withdrawal Due to Safety: A Review of the Data Supporting Withdrawal Decision","volume":"15","author":"NS Craveiro","year":"2020","journal-title":"Curr Drug Saf."},{"issue":"3","key":"pcbi.1013423.ref004","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/nrd3078","article-title":"How to improve R&D productivity: the pharmaceutical industry\u2019s grand challenge","volume":"9","author":"SM Paul","year":"2010","journal-title":"Nat Rev Drug Discov."},{"issue":"9","key":"pcbi.1013423.ref005","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1007\/s10822-011-9468-3","article-title":"Mixed learning algorithms and features ensemble in hepatotoxicity prediction","volume":"25","author":"CY Liew","year":"2011","journal-title":"J Comput Aided Mol Des."},{"issue":"1","key":"pcbi.1013423.ref006","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1093\/toxsci\/kfy121","article-title":"Predicting drug-induced liver injury using ensemble learning methods and molecular fingerprints","volume":"165","author":"H Ai","year":"2018","journal-title":"Toxicol Sci."},{"issue":"10","key":"pcbi.1013423.ref007","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1021\/acs.jcim.5b00238","article-title":"Deep learning for drug-induced liver injury","volume":"55","author":"Y Xu","year":"2015","journal-title":"J Chem Inf Model."},{"issue":"1","key":"pcbi.1013423.ref008","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab503","article-title":"ResNet18DNN: prediction approach of drug-induced liver injury by deep neural network with ResNet18","volume":"23","author":"Z Chen","year":"2022","journal-title":"Brief Bioinform."},{"key":"pcbi.1013423.ref009","doi-asserted-by":"crossref","first-page":"110239","DOI":"10.1016\/j.engappai.2025.110239","article-title":"Drug\u2013target affinity prediction using rotary encoding and information retention mechanisms","volume":"147","author":"Z Zhu","year":"2025","journal-title":"Engineering Applications of Artificial Intelligence."},{"issue":"4","key":"pcbi.1013423.ref010","doi-asserted-by":"crossref","first-page":"1558","DOI":"10.1049\/cit2.12194","article-title":"Associative learning mechanism for drug-target interaction prediction","volume":"8","author":"Z Zhu","year":"2023","journal-title":"CAAI Trans on Intel Tech."},{"issue":"7","key":"pcbi.1013423.ref011","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.2217\/14622416.7.7.1025","article-title":"Toxicogenomics in drug discovery and development: mechanistic analysis of compound\/class-dependent effects using the DrugMatrix database","volume":"7","author":"B Ganter","year":"2006","journal-title":"Pharmacogenomics."},{"key":"pcbi.1013423.ref012","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gku955","article-title":"Open TG-GATEs: a large-scale toxicogenomics database","volume":"43","author":"Y Igarashi","year":"2015","journal-title":"Nucleic Acids Res."},{"issue":"6","key":"pcbi.1013423.ref013","doi-asserted-by":"crossref","DOI":"10.1016\/j.cell.2017.10.049","article-title":"A next generation connectivity map: L1000 platform and the first 1,000,000 profiles","volume":"171","author":"A Subramanian","year":"2017","journal-title":"Cell."},{"issue":"4","key":"pcbi.1013423.ref014","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1016\/j.drudis.2016.02.015","article-title":"DILIrank: the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans","volume":"21","author":"M Chen","year":"2016","journal-title":"Drug Discov Today."},{"issue":"6","key":"pcbi.1013423.ref015","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.3390\/ijms21062114","article-title":"Computational models using multiple machine learning algorithms for predicting drug hepatotoxicity with the DILIrank dataset","volume":"21","author":"R Ancuceanu","year":"2020","journal-title":"Int J Mol Sci."},{"issue":"7","key":"pcbi.1013423.ref016","doi-asserted-by":"crossref","first-page":"2628","DOI":"10.1021\/acs.molpharmaceut.0c00326","article-title":"Comparing machine learning algorithms for predicting Drug-Induced Liver Injury (DILI)","volume":"17","author":"E Minerali","year":"2020","journal-title":"Mol Pharm."},{"key":"pcbi.1013423.ref017","doi-asserted-by":"crossref","first-page":"100580","DOI":"10.1016\/j.curtheres.2020.100580","article-title":"Drug-induced liver injury in critically ill children taking antiepileptic drugs: a retrospective study","volume":"92","author":"K Sridharan","year":"2020","journal-title":"Curr Ther Res Clin Exp."},{"issue":"1","key":"pcbi.1013423.ref018","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.drudis.2019.09.022","article-title":"Drug-induced liver injury severity and toxicity (DILIst): binary classification of 1279 drugs by human hepatotoxicity","volume":"25","author":"S Thakkar","year":"2020","journal-title":"Drug Discov Today."},{"key":"pcbi.1013423.ref019","doi-asserted-by":"crossref","first-page":"562677","DOI":"10.3389\/fbioe.2020.562677","article-title":"Deep learning on high-throughput transcriptomics to predict drug-induced liver injury","volume":"8","author":"T Li","year":"2020","journal-title":"Front Bioeng Biotechnol."},{"key":"pcbi.1013423.ref020","article-title":"Semi-supervised classification with graph convolutional networks","author":"T Kipf","year":"2017","journal-title":"arXiv preprint"},{"key":"pcbi.1013423.ref021","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.ymeth.2019.04.008","article-title":"Deep learning in bioinformatics: introduction, application, and perspective in the big data era","volume":"166","author":"Y Li","year":"2019","journal-title":"Methods."},{"issue":"13","key":"pcbi.1013423.ref022","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/bty294","article-title":"Modeling polypharmacy side effects with graph convolutional networks","volume":"34","author":"M Zitnik","year":"2018","journal-title":"Bioinformatics."},{"key":"pcbi.1013423.ref023","article-title":"Towards probabilistic generative models harnessing graph neural networks for disease-gene prediction","author":"V Singh","year":"2019","journal-title":"arXiv preprint"},{"key":"pcbi.1013423.ref024","doi-asserted-by":"crossref","first-page":"124647","DOI":"10.1016\/j.eswa.2024.124647","article-title":"Drug\u2013target binding affinity prediction model based on multi-scale diffusion and interactive learning","volume":"255","author":"Z Zhu","year":"2024","journal-title":"Expert Systems with Applications."},{"key":"pcbi.1013423.ref025","doi-asserted-by":"crossref","first-page":"107621","DOI":"10.1016\/j.compbiomed.2023.107621","article-title":"Drug-target affinity prediction method based on multi-scale information interaction and graph optimization","volume":"167","author":"Z Zhu","year":"2023","journal-title":"Comput Biol Med."},{"issue":"4","key":"pcbi.1013423.ref026","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","article-title":"A tutorial on spectral clustering","volume":"17","author":"U von Luxburg","year":"2007","journal-title":"Stat Comput."},{"key":"pcbi.1013423.ref027","article-title":"Graph learning-convolutional networks","author":"B Jiang","year":"2018","journal-title":"arXiv preprint"},{"issue":"1","key":"pcbi.1013423.ref028","article-title":"Distant metastasis identification based on optimized graph representation of gene interaction patterns","volume":"23","author":"R Su","year":"2022","journal-title":"Brief Bioinform."},{"issue":"1","key":"pcbi.1013423.ref029","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbae673","article-title":"Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics","volume":"26","author":"J Martorell-Marug\u00e1n","year":"2024","journal-title":"Brief Bioinform."},{"issue":"6","key":"pcbi.1013423.ref030","article-title":"ToxGIN: an In silico prediction model for peptide toxicity via graph isomorphism networks integrating peptide sequence and structure information","volume":"25","author":"Q Yu","year":"2024","journal-title":"Brief Bioinform."},{"issue":"5","key":"pcbi.1013423.ref031","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1038\/s41419-018-0507-z","article-title":"p53 attenuates acetaminophen-induced hepatotoxicity by regulating drug-metabolizing enzymes and transporter expression","volume":"9","author":"J Sun","year":"2018","journal-title":"Cell Death Dis."},{"issue":"1","key":"pcbi.1013423.ref032","doi-asserted-by":"crossref","first-page":"14614","DOI":"10.1038\/s41598-019-51175-z","article-title":"Hepatospecific ablation of p38\u03b1 MAPK governs liver regeneration through modulation of inflammatory response to CCl4-induced acute injury","volume":"9","author":"M Fortier","year":"2019","journal-title":"Sci Rep."},{"key":"pcbi.1013423.ref033","doi-asserted-by":"crossref","first-page":"101965","DOI":"10.1016\/j.redox.2021.101965","article-title":"G protein \u03b25-ATM complexes drive acetaminophen-induced hepatotoxicity","volume":"43","author":"A Pramanick","year":"2021","journal-title":"Redox Biol."},{"issue":"1","key":"pcbi.1013423.ref034","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s00441-011-1178-6","article-title":"Smad phosphoisoform signals in acute and chronic liver injury: similarities and differences between epithelial and mesenchymal cells","volume":"347","author":"K Matsuzaki","year":"2012","journal-title":"Cell Tissue Res."},{"issue":"12","key":"pcbi.1013423.ref035","doi-asserted-by":"crossref","first-page":"4687","DOI":"10.1002\/bit.27931","article-title":"An integrated biomimetic array chip for establishment of collagen-based 3D primary human hepatocyte model for prediction of clinical drug-induced liver injury","volume":"118","author":"R-R Xiao","year":"2021","journal-title":"Biotechnol Bioeng."},{"key":"pcbi.1013423.ref036","article-title":"ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader cover- age, improved performance, API functionality and decision support","volume":"52","author":"L Fu","year":"2024","journal-title":"Nucleic Acids Res."},{"issue":"20","key":"pcbi.1013423.ref037","doi-asserted-by":"crossref","first-page":"11397","DOI":"10.1021\/acs.jmedchem.0c00524","article-title":"Drug Induced Liver Injury (DILI). Mechanisms and medicinal chemistry avoidance\/mitigation strategies","volume":"63","author":"BH Norman","year":"2020","journal-title":"J Med Chem."},{"issue":"1","key":"pcbi.1013423.ref038","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1093\/bib\/bbz165","article-title":"Predicting drug-induced hepatotoxicity based on biological feature maps and diverse classification strategies","volume":"22","author":"R Su","year":"2021","journal-title":"Brief Bioinform."},{"issue":"1","key":"pcbi.1013423.ref039","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: tool for the unification of biology. The Gene Ontology Consortium","volume":"25","author":"M Ashburner","year":"2000","journal-title":"Nat Genet."},{"issue":"6","key":"pcbi.1013423.ref040","doi-asserted-by":"crossref","first-page":"561","DOI":"10.2174\/1389203715666140724090153","article-title":"Evaluating protein-protein interaction (PPI) networks for diseases pathway, target discovery, and drug-design using \u201cin silico pharmacology\u201d","volume":"15","author":"C Chakraborty","year":"2014","journal-title":"Curr Protein Pept Sci."},{"key":"pcbi.1013423.ref041","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkab835","article-title":"The STRING database in 2021 : customizable protein-protein networks, and functional characterization of user-uploaded gene\/measurement sets","volume":"49","author":"D Szklarczyk","year":"2021","journal-title":"Nucleic Acids Res."},{"issue":"2","key":"pcbi.1013423.ref042","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad027","article-title":"Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM","volume":"24","author":"S Tian","year":"2023","journal-title":"Brief Bioinform."}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1013423","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T17:42:35Z","timestamp":1757094155000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1013423"}},"subtitle":[],"editor":[{"given":"Juilee","family":"Thakar","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,9,5]]},"references-count":42,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9,5]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1013423","relation":{},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,5]]}}}