{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T18:16:20Z","timestamp":1787940980378,"version":"build-2784847793"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency CREST","doi-asserted-by":"crossref","award":["JPMJCR21N3"],"award-info":[{"award-number":["JPMJCR21N3"]}],"id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100023790","name":"ASPIRE","doi-asserted-by":"publisher","award":["JPMJAP24B1"],"award-info":[{"award-number":["JPMJAP24B1"]}],"id":[{"id":"10.13039\/100023790","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Promotion of Science Fellows","award":["24KJ1656"],"award-info":[{"award-number":["24KJ1656"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Complex gene regulatory networks (GRNs) underlie most disease processes, and understanding disease-specific network structures and dynamics is crucial for developing effective treatments. Yet, most database- and literature-based analyses of GRNs often treat gene regulations as context-independent interactions, overlooking how GRNs can differ depending on the disease type, cell lineage, or experimental condition.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In an attempt to improve on existing methods for leveraging knowledge present in the scientific literature, we developed a framework to assign quantitative, context-dependent weights to gene regulations extracted from literature. We demonstrate that the context-specific GRNs reconstructed with our method can effectively capture disease biology, showing strong correlation with transcriptomics across a wide range of diseases. Furthermore, we show that utilizing contextual information improves accuracy in drug\u2013target prediction tasks. Finally, we showcase the utility of the contextualized GRNs through the automated construction of an ordinary differential equation model of a breast cancer-specific signaling network. The large language model-based framework allows the integration of literature- and experimentally derived information and streamlines the process of assembling a biologically relevant and functional mathematical model. Our findings indicate the importance of considering the context when making biological predictions, and we demonstrate the use of natural language processing tools to effectively mine associations between gene regulations and biological contexts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>All reproducibility code is available at https:\/\/github.com\/okadalabipr\/context-dependent-GRNs, along with the automated mathematical model construction package at https:\/\/github.com\/okadalabipr\/BioMathForge. The dataset used in this study is available at Zenodo, DOI: 10.5281\/zenodo.16416117.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag101","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T12:47:30Z","timestamp":1772023650000},"source":"Crossref","is-referenced-by-count":0,"title":["Literature-derived, context-aware gene regulatory networks improve biological predictions and mathematical modeling"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0205-9709","authenticated-orcid":false,"given":"Masato","family":"Tsutsui","sequence":"first","affiliation":[{"name":"Institute for Protein Research, Osaka University , Suita, Osaka 565-0871,","place":["Japan"]},{"name":"Biological\/Pharmacological Research Laboratories, JT Central Pharmaceutical Research Institute , Takatsuki, Osaka 569-1125,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kiwamu","family":"Arakane","sequence":"additional","affiliation":[{"name":"Institute for Protein Research, Osaka University , Suita, Osaka 565-0871,","place":["Japan"]},{"name":"Department of Bioengineering, Imperial College London , London SW7 2AZ,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6210-8223","authenticated-orcid":false,"given":"Mariko","family":"Okada","sequence":"additional","affiliation":[{"name":"Institute for Protein Research, Osaka University , Suita, Osaka 565-0871,","place":["Japan"]},{"name":"Department of Bioengineering, Imperial College London , London SW7 2AZ,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"2026082813181957800_btag101-B1","doi-asserted-by":"crossref","first-page":"vbae042","DOI":"10.1093\/bioadv\/vbae042","article-title":"Extending BioMASS to construct mathematical models from external knowledge","volume":"4","author":"Arakane","year":"2024","journal-title":"Bioinform Adv"},{"key":"2026082813181957800_btag101-B2","doi-asserted-by":"crossref","first-page":"e106361","DOI":"10.1371\/journal.pone.0106361","article-title":"Predicting survey responses: how and why semantics shape survey statistics on organizational behaviour","volume":"9","author":"Arnulf","year":"2014","journal-title":"PLoS One"},{"key":"2026082813181957800_btag101-B3","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1038\/msb4100188","article-title":"Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses","volume":"3","author":"Birtwistle","year":"2007","journal-title":"Mol Syst Biol"},{"key":"2026082813181957800_btag101-B4","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/S0169-7552(98)00110-X","article-title":"The anatomy of a large-scale hypertextual web search engine","volume":"30","author":"Brin","year":"1998","journal-title":"Comput Netw ISDN Syst"},{"key":"2026082813181957800_btag101-B5","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/s41597-023-01960-3","article-title":"Building a knowledge graph to enable precision medicine","volume":"10","author":"Chandak","year":"2023","journal-title":"Sci Data"},{"key":"2026082813181957800_btag101-B6","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.1038\/s41467-024-46089-y","article-title":"Drug target prediction through deep learning functional representation of gene signatures","volume":"15","author":"Chen","year":"2024","journal-title":"Nat Commun"},{"key":"2026082813181957800_btag101-B7","doi-asserted-by":"crossref","first-page":"e1007617","DOI":"10.1371\/journal.pcbi.1007617","article-title":"BioConceptVec: creating and evaluating literature-based biomedical concept embeddings on a large scale","volume":"16","author":"Chen","year":"2020","journal-title":"PLoS Comput Biol"},{"key":"2026082813181957800_btag101-B8","doi-asserted-by":"crossref","first-page":"474","DOI":"10.26421\/JDI3.4-5","article-title":"Improved methods to aid unsupervised evidence-based fact checking for online health news","volume":"3","author":"Deka","year":"2022","journal-title":"JDI"},{"key":"2026082813181957800_btag101-B9","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/nature11247","article-title":"An integrated encyclopedia of DNA elements in the human genome","volume":"489","author":"ENCODE Project Consortium","year":"2012","journal-title":"Nature"},{"key":"2026082813181957800_btag101-B10","first-page":"282","volume-title":"Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence, AAAI\u201918\/IAAI\u201918\/EAAI\u201918","author":"Feng","year":"2018"},{"key":"2026082813181957800_btag101-B11","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.cels.2018.10.013","article-title":"Efficient parameter estimation enables the prediction of drug response using a mechanistic pan-cancer pathway model","volume":"7","author":"Fr\u00f6hlich","year":"2018","journal-title":"Cell Syst"},{"key":"2026082813181957800_btag101-B12","doi-asserted-by":"crossref","first-page":"e0303231","DOI":"10.1371\/journal.pone.0303231","article-title":"Large language model based framework for automated extraction of genetic interactions from unstructured data","volume":"19","author":"Gill","year":"2024","journal-title":"PLoS One"},{"key":"2026082813181957800_btag101-B13","doi-asserted-by":"crossref","first-page":"4649","DOI":"10.1093\/bioinformatics\/btaa560","article-title":"BioModels parameters: a treasure trove of parameter values from published systems biology models","volume":"36","author":"Glont","year":"2020","journal-title":"Bioinformatics"},{"key":"2026082813181957800_btag101-B14","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1093\/embo-reports\/kvf059","article-title":"Mining the bibliome: searching for a needle in a haystack?","volume":"3","author":"Grivell","year":"2002","journal-title":"EMBO Reports"},{"key":"2026082813181957800_btag101-B15","doi-asserted-by":"crossref","first-page":"2:1","DOI":"10.1145\/3458754","article-title":"Domain-specific language model pretraining for biomedical natural language processing","volume":"3","author":"Gu","year":"2022","journal-title":"ACM Trans Comput Healthcare"},{"key":"2026082813181957800_btag101-B16","doi-asserted-by":"crossref","first-page":"954","DOI":"10.15252\/msb.20177651","article-title":"From word models to executable models of signaling networks using automated assembly","volume":"13","author":"Gyori","year":"2017","journal-title":"Mol Syst Biol"},{"key":"2026082813181957800_btag101-B17","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.cels.2016.11.013","article-title":"Context specificity in causal signaling networks revealed by phosphoprotein profiling","volume":"4","author":"Hill","year":"2017","journal-title":"Cell Syst"},{"key":"2026082813181957800_btag101-B18","doi-asserted-by":"crossref","first-page":"3601","DOI":"10.1038\/s41591-024-03233-x","article-title":"A foundation model for clinician-centered drug repurposing","volume":"30","author":"Huang","year":"2024","journal-title":"Nat Med"},{"key":"2026082813181957800_btag101-B19","doi-asserted-by":"crossref","first-page":"2878","DOI":"10.3390\/cancers12102878","article-title":"A computational framework for prediction and analysis of cancer signaling dynamics from RNA sequencing data\u2014application to the ErbB receptor signaling pathway","volume":"12","author":"Imoto","year":"2020","journal-title":"Cancers (Basel)"},{"key":"2026082813181957800_btag101-B20","doi-asserted-by":"crossref","first-page":"103944","DOI":"10.1016\/j.isci.2022.103944","article-title":"A text-based computational framework for patient-specific modeling for classification of cancers","volume":"25","author":"Imoto","year":"2022","journal-title":"iScience"},{"key":"2026082813181957800_btag101-B21","doi-asserted-by":"crossref","first-page":"822606","DOI":"10.3389\/fsysb.2022.822606","article-title":"Model integration in computational biology: the role of reproducibility, credibility and utility","volume":"2","author":"Karr","year":"2022","journal-title":"Front Syst Biol"},{"key":"2026082813181957800_btag101-B22","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s12918-016-0266-3","article-title":"Challenges in horizontal model integration","volume":"10","author":"Kolczyk","year":"2016","journal-title":"BMC Syst Biol"},{"key":"2026082813181957800_btag101-B23","doi-asserted-by":"crossref","first-page":"104487","DOI":"10.1016\/j.jbi.2023.104487","article-title":"BioREx: improving biomedical relation extraction by leveraging heterogeneous datasets","volume":"146","author":"Lai","year":"2023","journal-title":"J Biomed Inform"},{"key":"2026082813181957800_btag101-B24","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","year":"2020","journal-title":"Bioinformatics"},{"key":"2026082813181957800_btag101-B25","doi-asserted-by":"crossref","first-page":"btad310","DOI":"10.1093\/bioinformatics\/btad310","article-title":"AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning","volume":"39","author":"Luo","year":"2023","journal-title":"Bioinformatics"},{"key":"2026082813181957800_btag101-B26","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1038\/s41390-025-04021-0","article-title":"Genomics and multiomics in the age of precision medicine","volume":"97","author":"Mani","year":"2025","journal-title":"Pediatr Res"},{"key":"2026082813181957800_btag101-B27","doi-asserted-by":"crossref","first-page":"110865","DOI":"10.1016\/j.jbc.2025.110865","article-title":"The receptor tyrosine kinase ErbB2\/HER2 governs CDK4 inhibitor sensitivity, timing, and irreversibility of the G1\/S transition","volume":"301","author":"Nagasato-Ichikawa","year":"2025","journal-title":"J Biol Chem"},{"key":"2026082813181957800_btag101-B28","doi-asserted-by":"crossref","first-page":"4045","DOI":"10.1074\/jbc.M608653200","article-title":"Quantitative transcriptional control of ErbB receptor signaling undergoes graded to biphasic response for cell differentiation","volume":"282","author":"Nagashima","year":"2007","journal-title":"J Biol Chem"},{"key":"2026082813181957800_btag101-B29","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1016\/j.cell.2010.03.054","article-title":"Ligand-specific c-fos expression emerges from the spatiotemporal control of ErbB network dynamics","volume":"141","author":"Nakakuki","year":"2010","journal-title":"Cell"},{"key":"2026082813181957800_btag101-B30","author":"National Library of Medicine","year":"2025"},{"key":"2026082813181957800_btag101-B31","author":"National Library of Medicine","year":"2025"},{"key":"2026082813181957800_btag101-B32","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1111\/gtc.13119","article-title":"Clover: an unbiased method for prioritizing differentially expressed genes using a data-driven approach","volume":"29","author":"Oba","year":"2024","journal-title":"Genes Cells"},{"key":"2026082813181957800_btag101-B33","doi-asserted-by":"crossref","first-page":"4512","DOI":"10.18653\/v1\/2020.emnlp-main.365","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"Reimers","year":"2020"},{"key":"2026082813181957800_btag101-B34","first-page":"3982","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"Reimers","year":"2019"},{"key":"2026082813181957800_btag101-B35","first-page":"MT89","article-title":"From transcriptomics to bibliomics","volume":"9","author":"Rihm","year":"2003","journal-title":"Med Sci Monit"},{"key":"2026082813181957800_btag101-B36","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1038\/s42256-025-01069-9","article-title":"Unifying multi-sample network inference from prior knowledge and omics data with CORNETO","volume":"7","author":"Rodriguez-Mier","year":"2025","journal-title":"Nat Mach Intell"},{"key":"2026082813181957800_btag101-B37","first-page":"114","article-title":"Communications to the editor","volume":"51","author":"Rogers","year":"1963","journal-title":"Bull Med Libr Assoc"},{"key":"2026082813181957800_btag101-B38","doi-asserted-by":"crossref","first-page":"e1013300","DOI":"10.1371\/journal.pcbi.1013300","article-title":"NeKo: a tool for automatic network construction from prior knowledge","volume":"21","author":"Ruscone","year":"2025","journal-title":"PLoS Comput Biol"},{"key":"2026082813181957800_btag101-B39","doi-asserted-by":"crossref","first-page":"e2006643","DOI":"10.1371\/journal.pbio.2006643","article-title":"Large-scale investigation of the reasons why potentially important genes are ignored","volume":"16","author":"Stoeger","year":"2018","journal-title":"PLoS Biol"},{"key":"2026082813181957800_btag101-B40","doi-asserted-by":"crossref","first-page":"1437","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":"Subramanian","year":"2017","journal-title":"Cell"},{"key":"2026082813181957800_btag101-B41","doi-asserted-by":"crossref","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"Subramanian","year":"2005","journal-title":"Proc Natl Acad Sci USA"},{"key":"2026082813181957800_btag101-B42","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1038\/ng.375","article-title":"The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line","volume":"41","author":"FANTOM Consortium, Suzuki H, Forrest ARR","year":"2009","journal-title":"Nat Genet"},{"key":"2026082813181957800_btag101-B43","doi-asserted-by":"crossref","first-page":"D638","DOI":"10.1093\/nar\/gkac1000","article-title":"The STRING database in 2023: protein\u2013protein association networks and functional enrichment analyses for any sequenced genome of interest","volume":"51","author":"Szklarczyk","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2026082813181957800_btag101-B44","doi-asserted-by":"crossref","first-page":"D325","DOI":"10.1093\/nar\/gkaa1113","article-title":"The Gene Ontology resource: enriching a GOld mine","volume":"49","author":"The Gene Ontology Consortium","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2026082813181957800_btag101-B45","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1038\/nmeth.4077","article-title":"OmniPath: guidelines and gateway for literature-curated signaling pathway resources","volume":"13","author":"T\u00fcrei","year":"2016","journal-title":"Nat Methods"},{"key":"2026082813181957800_btag101-B46","author":"Wang","year":"2025"},{"key":"2026082813181957800_btag101-B47","doi-asserted-by":"crossref","first-page":"W540","DOI":"10.1093\/nar\/gkae235","article-title":"PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge","volume":"52","author":"Wei","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2026082813181957800_btag101-B48","doi-asserted-by":"crossref","first-page":"D1236","DOI":"10.1093\/nar\/gkad961","article-title":"DiSignAtlas: an atlas of human and mouse disease signatures based on bulk and single-cell transcriptomics","volume":"52","author":"Zhai","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2026082813181957800_btag101-B49","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1093\/bib\/bbm045","article-title":"Frontiers of biomedical text mining: current progress","volume":"8","author":"Zweigenbaum","year":"2007","journal-title":"Brief Bioinform"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btag101\/67136908\/btag101.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/8\/btag101\/67136908\/btag101.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/8\/btag101\/67136908\/btag101.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T17:18:33Z","timestamp":1787937513000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btag101\/8499661"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,2,26]]},"references-count":49,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,8,3]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btag101","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2025.07.26.666734","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,8]]},"published":{"date-parts":[[2026,2,26]]},"article-number":"btag101"}}