{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T07:00:07Z","timestamp":1791874807238,"version":"4.3.4"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1013968","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000}}],"reference-count":29,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2025M771545"],"award-info":[{"award-number":["2025M771545"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013061","name":"Jilin Provincial Scientific and Technological Development Program","doi-asserted-by":"publisher","award":["20240101364JC"],"award-info":[{"award-number":["20240101364JC"]}],"id":[{"id":"10.13039\/501100013061","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    MicroRNAs (miRNAs) are pivotal regulators of drug resistance and sensitivity in cancer cells, functioning as tumor suppressors or oncogenes that modulate the cellular response to anticancer drugs. While experimental identification of miRNA-mediated drug resistance and sensitivity is both costly and laborious, computational methods present a promising alternative. Recent advances in pre-trained language models (PLMs) offer new opportunities to leverage large-scale unlabeled biomolecular data for enhanced relationship prediction. In this study, we introduce PLMF-MDA, a PLM-based cross-modal fusion model designed to predict miRNA-drug resistance (MDR) and miRNA-drug sensitivity (MDS) associations. PLMF-MDA integrates miRNA and drug multimodal embeddings derived from PLMs and intrinsic feature extractors, and employs a cross-modal attention fusion module to adaptively capture key interactions between modalities. To evaluate the performance of the approach, we manually constructed two benchmark datasets. Experimental results demonstrate that the PLMF-MDA achieves superior prediction performance. Furthermore, case studies on anticancer drug docetaxel and gefitinib demonstrate its potential in discovering novel MDR (MDS) associations. All data and source code are available on GitHub:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/sheng-n\/PLMF-MDA\" xlink:type=\"simple\">https:\/\/github.com\/sheng-n\/PLMF-MDA<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1013968","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:44:11Z","timestamp":1770749051000},"page":"e1013968","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["A pre-trained language model-based cross-modal fusion framework for predicting miRNA-drug resistance and sensitivity associations"],"prefix":"10.1371","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0306-9009","authenticated-orcid":true,"given":"Nan","family":"Sheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunzhi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenju","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3233-3777","authenticated-orcid":true,"given":"Lan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4751-0708","authenticated-orcid":true,"given":"Yan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"issue":"16","key":"pcbi.1013968.ref001","doi-asserted-by":"crossref","first-page":"3029","DOI":"10.1002\/cncr.33587","article-title":"The ever-increasing importance of cancer as a leading cause of premature death worldwide","volume":"127","author":"F Bray","year":"2021","journal-title":"Cancer."},{"issue":"1","key":"pcbi.1013968.ref002","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cllc.2021.08.011","article-title":"The combiome hypothesis: selecting optimal treatment for cancer patients","volume":"23","author":"FR Hirsch","year":"2022","journal-title":"Clin Lung Cancer."},{"issue":"1","key":"pcbi.1013968.ref003","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1038\/s41392-024-01856-7","article-title":"Exploring treatment options in cancer: tumor treatment strategies","volume":"9","author":"B Liu","year":"2024","journal-title":"Signal Transduct Target Ther."},{"issue":"1","key":"pcbi.1013968.ref004","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/s13148-018-0587-8","article-title":"The role and mechanisms of action of microRNAs in cancer drug resistance","volume":"11","author":"W Si","year":"2019","journal-title":"Clin Epigenetics."},{"issue":"5","key":"pcbi.1013968.ref005","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.1007\/s10565-021-09675-0","article-title":"microRNA-301b-3p from mesenchymal stem cells-derived extracellular vesicles inhibits TXNIP to promote multidrug resistance of gastric cancer cells","volume":"39","author":"T Zhu","year":"2023","journal-title":"Cell Biol Toxicol."},{"issue":"8","key":"pcbi.1013968.ref006","doi-asserted-by":"crossref","first-page":"6819","DOI":"10.1007\/s11033-023-08599-8","article-title":"MiR-590-5p promotes cisplatin resistance via targeting hMSH2 in ovarian cancer","volume":"50","author":"B Li","year":"2023","journal-title":"Mol Biol Rep."},{"key":"pcbi.1013968.ref007","doi-asserted-by":"crossref","first-page":"154765","DOI":"10.1016\/j.prp.2023.154765","article-title":"MiR-375 promotes cisplatin sensitivity of lung adenocarcinoma","volume":"249","author":"S Du","year":"2023","journal-title":"Pathol Res Pract."},{"key":"pcbi.1013968.ref008","doi-asserted-by":"crossref","first-page":"154765","DOI":"10.1016\/j.prp.2023.154765","article-title":"MiR-375 promotes cisplatin sensitivity of lung adenocarcinoma","volume":"249","author":"S Du","year":"2023","journal-title":"Pathol Res Pract."},{"issue":"3","key":"pcbi.1013968.ref009","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1093\/bioinformatics\/btz621","article-title":"Graph convolution for predicting associations between miRNA and drug resistance","volume":"36","author":"Y-A Huang","year":"2020","journal-title":"Bioinformatics."},{"issue":"23","key":"pcbi.1013968.ref010","doi-asserted-by":"crossref","first-page":"5929","DOI":"10.1021\/acs.jcim.2c01060","article-title":"Dual-channel heterogeneous graph neural network for predicting microRNA-mediated drug sensitivity","volume":"62","author":"L Deng","year":"2022","journal-title":"J Chem Inf Model."},{"issue":"4","key":"pcbi.1013968.ref011","article-title":"GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learning","volume":"24","author":"J Wei","year":"2023","journal-title":"Brief Bioinform."},{"issue":"15","key":"pcbi.1013968.ref012","doi-asserted-by":"crossref","first-page":"3676","DOI":"10.1021\/acs.jcim.2c00367","article-title":"Graph neural network with self-supervised learning for noncoding RNA-drug resistance association prediction","volume":"62","author":"J Zheng","year":"2022","journal-title":"J Chem Inf Model."},{"key":"pcbi.1013968.ref013","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.future.2024.06.010","article-title":"HGGN: prediction of microRNA-Mediated drug sensitivity based on interpretable heterogeneous graph global-attention network","volume":"160","author":"J Liu","year":"2024","journal-title":"Future Generation Computer Systems."},{"issue":"11","key":"pcbi.1013968.ref014","doi-asserted-by":"crossref","first-page":"5317","DOI":"10.1021\/acs.jcim.5c00038","article-title":"Deep-learning-based integration of sequence and structure information for efficiently predicting miRNA\u2013drug associations","volume":"65","author":"N Sheng","year":"2025","journal-title":"J Chem Inf Model."},{"issue":"5","key":"pcbi.1013968.ref015","doi-asserted-by":"crossref","DOI":"10.1007\/s11704-024-3862-1","article-title":"Predicting miRNA-drug interactions via dual-channel network based on TCN and BiLSTM","volume":"19","author":"X Zhang","year":"2024","journal-title":"Front Comput Sci."},{"issue":"11","key":"pcbi.1013968.ref016","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1013703","article-title":"Multi-view fusion based on graph convolutional network with attention mechanism for predicting miRNA related to drugs","volume":"21","author":"N Sheng","year":"2025","journal-title":"PLoS Comput Biol."},{"key":"pcbi.1013968.ref017","doi-asserted-by":"crossref","first-page":"126879","DOI":"10.1016\/j.eswa.2025.126879","article-title":"MGHSTCKW: Predicting miRNA\u2013drug sensitivity association using hypergraph sparse transformer and hypergraph-induced contrastive learning based on meta-path","volume":"274","author":"D Ouyang","year":"2025","journal-title":"Expert Systems with Applications."},{"issue":"2","key":"pcbi.1013968.ref018","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3605943","article-title":"Recent advances in natural language processing via large pre-trained language models: a survey","volume":"56","author":"B Min","year":"2023","journal-title":"ACM Comput Surv."},{"key":"pcbi.1013968.ref019","doi-asserted-by":"crossref","unstructured":"Chen J, Hu Z, Sun S, Tan Q, Wang Y, Yu Q. Interpretable RNA foundation model from unannotated data for highly accurate RNA structure and function predictions. arXiv preprint 2022. http:\/\/dx.doi.org\/10.48550\/arXiv.2204.00300","DOI":"10.1101\/2022.08.06.503062"},{"key":"pcbi.1013968.ref020","unstructured":"Ahmad W, Simon E, Chithrananda S, Grand G, Ramsundar B. Chemberta-2: towards chemical foundation models. arXiv preprint 2022. http:\/\/dx.doi.org\/10.48550\/arXiv.2209.01712"},{"key":"pcbi.1013968.ref021","article-title":"ncRNADrug: a database for validated and predicted ncRNAs associated with drug resistance and targeted by drugs","volume":"52","author":"X Cao","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"pcbi.1013968.ref022","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gky1141","article-title":"miRBase: from microRNA sequences to function","volume":"47","author":"A Kozomara","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"pcbi.1013968.ref023","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkad976","article-title":"DrugBank 6.0: the DrugBank knowledgebase for 2024","volume":"52","author":"C Knox","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"pcbi.1013968.ref024","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. Advances in neural information processing systems. 2017;30. https:\/\/doi.org\/10.48550\/arXiv.1706.03762"},{"key":"pcbi.1013968.ref025","doi-asserted-by":"crossref","first-page":"1201934","DOI":"10.3389\/fgene.2023.1201934","article-title":"Prediction of small molecule drug-miRNA associations based on GNNs and CNNs","volume":"14","author":"Z Niu","year":"2023","journal-title":"Front Genet."},{"issue":"1","key":"pcbi.1013968.ref026","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1186\/s12859-023-05460-4","article-title":"SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features","volume":"24","author":"S Pan","year":"2023","journal-title":"BMC Bioinformatics."},{"issue":"8","key":"pcbi.1013968.ref027","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1093\/bioinformatics\/btaa921","article-title":"GraphDTA: predicting drug-target binding affinity with graph neural networks","volume":"37","author":"T Nguyen","year":"2021","journal-title":"Bioinformatics."},{"issue":"17","key":"pcbi.1013968.ref028","doi-asserted-by":"crossref","first-page":"4247","DOI":"10.1021\/acs.jpclett.1c00867","article-title":"ML-DTI: mutual learning mechanism for interpretable drug-target interaction prediction","volume":"12","author":"Z Yang","year":"2021","journal-title":"J Phys Chem Lett."},{"issue":"12","key":"pcbi.1013968.ref029","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1039\/D2AN00171C","article-title":"Cancer drug resistance related microRNAs: recent advances in detection methods","volume":"147","author":"X-Y Hu","year":"2022","journal-title":"Analyst."}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1013968","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1013968","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T18:44:38Z","timestamp":1771440278000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1013968"}},"subtitle":[],"editor":[{"given":"Wei","family":"Lan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,2,10]]},"references-count":29,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2,10]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1013968","relation":{},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,10]]}}}