{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T08:03:28Z","timestamp":1789632208845,"version":"build-2803163510"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,9,6]],"date-time":"2027-09-06T00:00:00Z","timestamp":1820188800000},"content-version":"am","delay-in-days":340,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100006827","name":"Florida Department of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006827","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS-2318210"],"award-info":[{"award-number":["CNS-2318210"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R35GM128753"],"award-info":[{"award-number":["R35GM128753"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008275","name":"University of Florida Health","doi-asserted-by":"publisher","award":["AGR DTD 01-30-2015"],"award-info":[{"award-number":["AGR DTD 01-30-2015"]}],"id":[{"id":"10.13039\/100008275","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100001287","name":"The Elsa U Pardee Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001287","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Biomedical Informatics"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.jbi.2026.105099","type":"journal-article","created":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T06:41:37Z","timestamp":1788676897000},"page":"105099","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["GIN-CRC-Pareto: A graph-based pareto-optimized multi-task learning framework to identify miRNA-target interactions in colorectal cancer"],"prefix":"10.1016","volume":"182","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5433-6777","authenticated-orcid":false,"given":"Lin","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongru","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7955-197X","authenticated-orcid":false,"given":"Mingyi","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1403-0396","authenticated-orcid":false,"given":"Rui","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jbi.2026.105099_b0005","series-title":"Cancer Facts & Figures 2024","author":"American Cancer Society","year":"2024"},{"key":"10.1016\/j.jbi.2026.105099_b0010","series-title":"Colorectal Cancer Facts & Figures 2023-2025","author":"American Cancer Society","year":"2022"},{"key":"10.1016\/j.jbi.2026.105099_b0015","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1038\/s41575-019-0189-8","article-title":"Global burden of colorectal cancer: emerging trends, risk factors and prevention strategies","volume":"16","author":"Keum","year":"2019","journal-title":"Nat. Rev. Gastroenterol. Hepatol."},{"key":"10.1016\/j.jbi.2026.105099_b0020","doi-asserted-by":"crossref","first-page":"375","DOI":"10.14348\/molcells.2016.0013","article-title":"MicroRNA target recognition: insights from transcriptome-wide non-canonical interactions","volume":"39","author":"Seok","year":"2016","journal-title":"Mol. Cells"},{"key":"10.1016\/j.jbi.2026.105099_b0025","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.tins.2008.12.003","article-title":"Alterations of the microRNA network cause neurodegenerative disease","volume":"32","author":"H\u00e9bert","year":"2009","journal-title":"Trends Neurosci."},{"key":"10.1016\/j.jbi.2026.105099_b0030","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-1-4939-6524-3_1","article-title":"MiRNA biogenesis and regulation of diseases: an overview","volume":"1509","author":"Vishnoi","year":"2017","journal-title":"Methods Mol. Biol."},{"key":"10.1016\/j.jbi.2026.105099_b0035","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1186\/s12935-015-0185-1","article-title":"MicroRNA (miRNA) in cancer","volume":"15","author":"Reddy","year":"2015","journal-title":"Cancer Cell Int."},{"key":"10.1016\/j.jbi.2026.105099_b0040","doi-asserted-by":"crossref","first-page":"2577","DOI":"10.1016\/j.jacc.2016.09.945","article-title":"MicroRNAs in cardiovascular disease","volume":"68","author":"Barwari","year":"2016","journal-title":"J. Am. Coll. Cardiol."},{"key":"10.1016\/j.jbi.2026.105099_b0045","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1007\/s10555-023-10158-3","article-title":"Colorectal cancer: a comprehensive review of carcinogenesis, diagnosis, and novel strategies for classified treatments","volume":"43","author":"Abedizadeh","year":"2024","journal-title":"Cancer Metastasis Rev."},{"key":"10.1016\/j.jbi.2026.105099_b0050","doi-asserted-by":"crossref","first-page":"338","DOI":"10.3390\/genes15030338","article-title":"miRNA as a Biomarker for the Early Detection of Colorectal Cancer","volume":"15","author":"Coleman","year":"2024","journal-title":"Genes (Basel)"},{"key":"10.1016\/j.jbi.2026.105099_b0055","doi-asserted-by":"crossref","first-page":"7071","DOI":"10.1002\/jcp.30378","article-title":"MicroRNA-124-3p suppresses PD-L1 expression and inhibits tumorigenesis of colorectal cancer cells via modulating STAT3 signaling","volume":"236","author":"Roshani Asl","year":"2021","journal-title":"J. Cell. Physiol."},{"key":"10.1016\/j.jbi.2026.105099_b0060","first-page":"5338","article-title":"miRNA-708 functions as a tumor suppressor in colorectal cancer by targeting ZEB1 through Akt\/mTOR signaling pathway","volume":"11","author":"Sun","year":"2019","journal-title":"Am. J. Transl. Res."},{"key":"10.1016\/j.jbi.2026.105099_b0065","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1158\/1541-7786.MCR-18-0831","article-title":"MiRNA-148a-3p regulates immunosuppression in DNA mismatch repair-deficient colorectal cancer by targeting PD-L1","volume":"17","author":"Ashizawa","year":"2019","journal-title":"Mol. Cancer Res."},{"key":"10.1016\/j.jbi.2026.105099_b0070","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.05005","article-title":"Predicting effective microRNA target sites in mammalian mRNAs","volume":"4","author":"Agarwal","year":"2015","journal-title":"Elife"},{"key":"10.1016\/j.jbi.2026.105099_b0075","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1101\/gr.082701.108","article-title":"Most mammalian mRNAs are conserved targets of microRNAs","volume":"19","author":"Friedman","year":"2009","journal-title":"Genome Res."},{"key":"10.1016\/j.jbi.2026.105099_b0080","doi-asserted-by":"crossref","first-page":"e85","DOI":"10.1371\/journal.pbio.0030085","article-title":"Principles of microRNA-target recognition","volume":"3","author":"Brennecke","year":"2005","journal-title":"PLoS Biol."},{"key":"10.1016\/j.jbi.2026.105099_b0085","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1101\/gr.205146.116","article-title":"microRNA target prediction programs predict many false positives","volume":"27","author":"Pinz\u00f3n","year":"2017","journal-title":"Genome Res."},{"key":"10.1016\/j.jbi.2026.105099_b0090","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1038\/nature07242","article-title":"The impact of microRNAs on protein output","volume":"455","author":"Baek","year":"2008","journal-title":"Nature"},{"key":"10.1016\/j.jbi.2026.105099_b0095","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1038\/nature07228","article-title":"Widespread changes in protein synthesis induced by microRNAs","volume":"455","author":"Selbach","year":"2008","journal-title":"Nature"},{"key":"10.1016\/j.jbi.2026.105099_b0100","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1016\/j.molcel.2014.03.049","article-title":"Unambiguous identification of miRNA:target site interactions by different types of ligation reactions","volume":"54","author":"Grosswendt","year":"2014","journal-title":"Mol. Cell"},{"key":"10.1016\/j.jbi.2026.105099_b0105","doi-asserted-by":"crossref","first-page":"10010","DOI":"10.1073\/pnas.1017386108","article-title":"Cross-linking, ligation, and sequencing of hybrids reveals RNA-RNA interactions in yeast","volume":"108","author":"Kudla","year":"2011","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"10.1016\/j.jbi.2026.105099_b0110","doi-asserted-by":"crossref","first-page":"865","DOI":"10.2174\/0929867328666210804090224","article-title":"Recent development of bioinformatics tools for microRNA target prediction","volume":"29","author":"Khatun","year":"2022","journal-title":"Curr. Med. Chem."},{"key":"10.1016\/j.jbi.2026.105099_b0115","first-page":"1154","article-title":"Integrative approaches for analysis of mRNA and microRNA high-throughput data, Comput. Struct","volume":"19","author":"Nazarov","year":"2021","journal-title":"Biotechnol. J."},{"key":"10.1016\/j.jbi.2026.105099_b0120","doi-asserted-by":"crossref","first-page":"3781","DOI":"10.1093\/bioinformatics\/bty424","article-title":"DeepMirTar: a deep-learning approach for predicting human miRNA targets","volume":"34","author":"Wen","year":"2018","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jbi.2026.105099_b0125","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1006185","article-title":"miRAW: a deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts","volume":"14","author":"Pla","year":"2018","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.jbi.2026.105099_b0130","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab470","article-title":"Liu, preMLI: a pre-trained method to uncover microRNA\u2013lncRNA potential interactions","volume":"23","author":"Yu","year":"2022","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.jbi.2026.105099_b0135","doi-asserted-by":"crossref","first-page":"3020","DOI":"10.1016\/j.csbj.2024.07.014","article-title":"Gra-CRC-miRTar: the pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer","volume":"23","author":"Yin","year":"2024","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.jbi.2026.105099_b0140","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btae011","article-title":"SeedMatchR: identify off-target effects mediated by siRNA seed regions in RNA-seq experiments","volume":"40","author":"Cazares","year":"2024","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jbi.2026.105099_b0145","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1093\/bioinformatics\/btab733","article-title":"TargetNet: functional microRNA target prediction with deep neural networks","volume":"38","author":"Min","year":"2022","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jbi.2026.105099_b0150","unstructured":"X. Lin, H.-L. Zhen, Z. Li, Q. Zhang, S. Kwong, Pareto Multi-Task Learning, ArXiv [Cs.LG] (2019). http:\/\/arxiv.org\/abs\/1912.12854."},{"issue":"2022","key":"10.1016\/j.jbi.2026.105099_b0155","doi-asserted-by":"crossref","first-page":"D222","DOI":"10.1093\/nar\/gkab1079","article-title":"miRTarBase update 2022: an informative resource for experimentally validated miRNA\u2013target interactions","volume":"50","author":"Huang","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jbi.2026.105099_b0160","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.ymeth.2013.10.015","article-title":"Hyb: a bioinformatics pipeline for the analysis of CLASH (crosslinking, ligation and sequencing of hybrids) data","volume":"65","author":"Travis","year":"2014","journal-title":"Methods"},{"key":"10.1016\/j.jbi.2026.105099_b0165","doi-asserted-by":"crossref","DOI":"10.1093\/database\/baaa062","article-title":"a comprehensive update on curation, resources and tools","volume":"2020","author":"Schoch","year":"2020","journal-title":"Database (Oxford)"},{"key":"10.1016\/j.jbi.2026.105099_b0170","article-title":"CLASHub: an integrated database and analytical platform for microRNA-target interaction","author":"Li","year":"2026","journal-title":"BioRxiv"},{"key":"10.1016\/j.jbi.2026.105099_b0175","doi-asserted-by":"crossref","first-page":"10","DOI":"10.14806\/ej.17.1.200","article-title":"Cutadapt removes adapter sequences from high-throughput sequencing reads","volume":"17","author":"Martin","year":"2011","journal-title":"Embnet. J."},{"key":"10.1016\/j.jbi.2026.105099_b0180","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1093\/bioinformatics\/btt593","article-title":"PEAR: a fast and accurate illumina paired-End reAd mergeR","volume":"30","author":"Zhang","year":"2014","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jbi.2026.105099_b0185","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1006\/geno.1997.4995","article-title":"Comparison of DNA sequences with protein sequences","volume":"46","author":"Pearson","year":"1997","journal-title":"Genomics"},{"key":"10.1016\/j.jbi.2026.105099_b0190","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.cell.2013.03.043","article-title":"Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding","volume":"153","author":"Helwak","year":"2013","journal-title":"Cell"},{"key":"10.1016\/j.jbi.2026.105099_b0195","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/nmeth.1923","article-title":"Fast gapped-read alignment with Bowtie 2","volume":"9","author":"Langmead","year":"2012","journal-title":"Nat. Methods"},{"key":"10.1016\/j.jbi.2026.105099_b0200","doi-asserted-by":"crossref","first-page":"D155","DOI":"10.1093\/nar\/gky1141","article-title":"miRBase: from microRNA sequences to function","volume":"47","author":"Kozomara","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jbi.2026.105099_b0205","doi-asserted-by":"crossref","first-page":"D988","DOI":"10.1093\/nar\/gkab1049","article-title":"Ensembl 2022","volume":"50","author":"Cunningham","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jbi.2026.105099_b0210","unstructured":"K. Xu, W. Hu, J. Leskovec, S. Jegelka, How Powerful are Graph Neural Networks?, ArXiv [Cs.LG] (2018). http:\/\/arxiv.org\/abs\/1810.00826."},{"key":"10.1016\/j.jbi.2026.105099_b0215","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1002\/nav.3800030109","article-title":"An algorithm for quadratic programming","volume":"3","author":"Frank","year":"1956","journal-title":"Naval Res. Log. Quart."},{"key":"10.1016\/j.jbi.2026.105099_b0220","unstructured":"A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Automatic differentiation in pytorch, (2017). https:\/\/openreview.net\/forum?id=BJJsrmfCZ."},{"key":"10.1016\/j.jbi.2026.105099_b0225","unstructured":"M. Wang, D. Zheng, Z. Ye, Q. Gan, M. Li, X. Song, J. Zhou, C. Ma, L. Yu, Y. Gai, T. Xiao, T. He, G. Karypis, J. Li, Z. Zhang, Deep Graph Library: A graph-centric, highly-performant package for graph neural networks, ArXiv [Cs.LG] (2019). http:\/\/arxiv.org\/abs\/1909.01315."},{"key":"10.1016\/j.jbi.2026.105099_b0230","unstructured":"R. Rehurek, P. Sojka, Software Framework for Topic Modelling with Large Corpora, in: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, ELRA, 2010: pp. 45\u201350."},{"key":"10.1016\/j.jbi.2026.105099_b0235","first-page":"2825","article-title":"Scikit-learn: machine learning in Python, the","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.jbi.2026.105099_b0240","doi-asserted-by":"crossref","first-page":"4372","DOI":"10.3390\/molecules25194372","article-title":"LncMirNet: predicting LncRNA-miRNA interaction based on deep learning of ribonucleic acid sequences","volume":"25","author":"Yang","year":"2020","journal-title":"Molecules"},{"key":"10.1016\/j.jbi.2026.105099_b0245","doi-asserted-by":"crossref","first-page":"2986","DOI":"10.1093\/bioinformatics\/btaa074","article-title":"PmliPred: a method based on hybrid model and fuzzy decision for plant miRNA-lncRNA interaction prediction","volume":"36","author":"Kang","year":"2020","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jbi.2026.105099_b0250","first-page":"44","article-title":"PmliHFM: predicting plant miRNA-lncRNA interactions with hybrid feature mining network","volume":"15","author":"Chen","year":"2023","journal-title":"Interdiscip. Sci."},{"key":"10.1016\/j.jbi.2026.105099_b0255","unstructured":"A. Navon, A. Shamsian, I. Achituve, H. Maron, K. Kawaguchi, G. Chechik, E. Fetaya, Multi-task learning as a bargaining game, ArXiv [Cs.LG] (2022). https:\/\/doi.org\/10.48550\/arXiv.2202.01017."},{"key":"10.1016\/j.jbi.2026.105099_b0260","unstructured":"M. Sharif, G. Han, W. Liu, X. Huang, Cultivating multidisciplinary research and education on GPU infrastructure for mid-South institutions at the University of Memphis: Practice and challenge, ArXiv [Cs.DC] http:\/\/arxiv.org\/abs\/2504.14786."}],"container-title":["Journal of Biomedical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046426001231?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046426001231?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T07:16:11Z","timestamp":1789629371000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1532046426001231"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":52,"alternative-id":["S1532046426001231"],"URL":"https:\/\/doi.org\/10.1016\/j.jbi.2026.105099","relation":{},"ISSN":["1532-0464"],"issn-type":[{"value":"1532-0464","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"GIN-CRC-Pareto: A graph-based pareto-optimized multi-task learning framework to identify miRNA-target interactions in colorectal cancer","name":"articletitle","label":"Article Title"},{"value":"Journal of Biomedical Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jbi.2026.105099","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"105099"}}