{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T08:35:15Z","timestamp":1770539715230,"version":"3.49.0"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T00:00:00Z","timestamp":1750118400000},"content-version":"vor","delay-in-days":47,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271132"],"award-info":[{"award-number":["62271132"]}],"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":["62371423"],"award-info":[{"award-number":["62371423"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Technologies Research and Development Program of China","award":["2022YFF1202100"],"award-info":[{"award-number":["2022YFF1202100"]}]},{"DOI":"10.13039\/501100005046","name":"Heilongjiang Province Science Foundation","doi-asserted-by":"crossref","award":["LH2024F001"],"award-info":[{"award-number":["LH2024F001"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100005046","name":"Heilongjiang Province Science Foundation","doi-asserted-by":"crossref","award":["ZD2024F001"],"award-info":[{"award-number":["ZD2024F001"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) play an important role in the development of complex human diseases by collaboratively regulating gene transcription and expression. Therefore, identifying lncRNA-miRNA interactions (LMIs) is essential for diagnosing and treating complex human diseases. Because identifying LMIs with wet experiments is time-consuming and labor-intensive, some computational methods have been developed to infer LMIs. However, these approaches excel at utilizing single-modal information but struggle to integrate multimodal data from lncRNAs and miRNAs, which is essential for uncovering complex patterns in LMIs, ultimately limiting their performance. Therefore, this article proposes a novel multimodal contrastive representation learning model (MCRLMI) for LMI predictions. The model fully integrates multi-source similarity information and sequence encodings of lncRNAs and miRNAs. It leverages a graph convolutional network (GCN) and a Transformer to capture local neighborhood structural features and long-distance dependencies, respectively, enabling the collaborative modeling of structural and semantic information. Subsequently, to effectively integrate multimodal characteristics with encoded information, a multichannel attention mechanism and contrastive learning are introduced to fuse the extracted features. Finally, a Kolmogorov\u2013Arnold Network (KAN) is trained with the optimized embeddings to predict LMIs. Extensive experiments show that the proposed MCRLMI consistently outperforms existing methods. Moreover, case studies further validate the potential of MCRLMI to identify novel LMIs in practical applications.<\/jats:p>","DOI":"10.1093\/bib\/bbaf281","type":"journal-article","created":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T18:06:41Z","timestamp":1750961201000},"source":"Crossref","is-referenced-by-count":1,"title":["Enhancing LncRNA-miRNA interaction prediction with multimodal contrastive representation learning"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6968-4354","authenticated-orcid":false,"given":"Zhixia","family":"Teng","sequence":"first","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , 150040, Harbin ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3765-1992","authenticated-orcid":false,"given":"Zhaowen","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , 150040, Harbin ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9634-8164","authenticated-orcid":false,"given":"Murong","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , 150040, Harbin ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7381-2374","authenticated-orcid":false,"given":"Guohua","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , 150040, Harbin ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0945-8168","authenticated-orcid":false,"given":"Zhen","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University , 450001, Zhengzhou ,","place":["China"]},{"name":"Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China , 324000, Quzhou ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7219-0999","authenticated-orcid":false,"given":"Yuming","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , 150040, Harbin ,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,6,17]]},"reference":[{"key":"2025062614063581000_ref1","doi-asserted-by":"publisher","first-page":"90","DOI":"10.3389\/fgene.2020.00090","article-title":"Ncresnet: Noncoding ribonucleic acid prediction based on a deep resident network of ribonucleic acid sequences","volume":"11","author":"Yang","year":"2020","journal-title":"Front Genet"},{"key":"2025062614063581000_ref2","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.molcel.2017.09.015","article-title":"A peptide encoded by a putative lncrna hoxb-as3 suppresses colon cancer growth","volume":"68","author":"Huang","year":"2017","journal-title":"Mol Cell"},{"key":"2025062614063581000_ref3","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1111\/tpj.13408","article-title":"Comparative transcriptome analysis between resistant and susceptible tomato allows the identification of lnc rna 16397 conferring resistance to phytophthora infestans by co-expressing glutaredoxin","volume":"89","author":"Cui","year":"2017","journal-title":"Plant J"},{"key":"2025062614063581000_ref4","doi-asserted-by":"publisher","first-page":"273","DOI":"10.3390\/genes10040273","article-title":"Prediction of long non-coding rnas based on deep learning","volume":"10","author":"Liu","year":"2019","journal-title":"Genes"},{"key":"2025062614063581000_ref5","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac463","article-title":"Predicting the potential human lncrna\u2013mirna interactions based on graph convolution network with conditional random field","volume":"23","author":"Wang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025062614063581000_ref6","doi-asserted-by":"publisher","first-page":"5661","DOI":"10.1038\/onc.2017.184","article-title":"Lncrna-mediated regulation of cell signaling in cancer","volume":"36","author":"Peng","year":"2017","journal-title":"Oncogene"},{"key":"2025062614063581000_ref7","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s12920-018-0429-8","article-title":"Novel link prediction for large-scale mirna-lncrna interaction network in a bipartite graph","volume":"11","author":"Huang","year":"2018","journal-title":"BMC Med Genomics"},{"key":"2025062614063581000_ref8","doi-asserted-by":"publisher","first-page":"1597","DOI":"10.1111\/liv.12746","article-title":"Long non-coding rna hottip is frequently up-regulated in hepatocellular carcinoma and is targeted by tumour suppressive mir-125b","volume":"35","author":"Tsang","year":"2015","journal-title":"Liver Int"},{"key":"2025062614063581000_ref9","doi-asserted-by":"publisher","first-page":"570","DOI":"10.3390\/cancers13030570","article-title":"Functional interaction among lncrna hotair and micrornas in cancer and other human diseases","volume":"13","author":"Cantile","year":"2021","journal-title":"Cancers"},{"key":"2025062614063581000_ref10","doi-asserted-by":"publisher","first-page":"bbac411","DOI":"10.1093\/bib\/bbac411","article-title":"Ncrnainter: A novel strategy based on graph neural network to discover interactions between lncrna and mirna","volume":"23","author":"Zhang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025062614063581000_ref11","doi-asserted-by":"publisher","first-page":"bbab470","DOI":"10.1093\/bib\/bbab470","article-title":"Premli: A pre-trained method to uncover microrna\u2013lncrna potential interactions","volume":"23","author":"Xinyu","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025062614063581000_ref12","doi-asserted-by":"publisher","first-page":"bbac357","DOI":"10.1093\/bib\/bbac357","article-title":"A merged molecular representation deep learning method for blood\u2013brain barrier permeability prediction","volume":"23","author":"Tang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025062614063581000_ref13","doi-asserted-by":"publisher","first-page":"1302","DOI":"10.3390\/sym14071302","article-title":"Recent deep learning methodology development for rna\u2013rna interaction prediction","volume":"14","author":"Fang","year":"2022","journal-title":"Symmetry"},{"key":"2025062614063581000_ref14","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.jtbi.2015.08.025","article-title":"Identification of microrna precursor with the degenerate k-tuple or kmer strategy","volume":"385","author":"Liu","year":"2015","journal-title":"J Theor Biol"},{"key":"2025062614063581000_ref15","doi-asserted-by":"publisher","first-page":"e43","DOI":"10.1093\/nar\/gkz087","article-title":"Cppred: Coding potential prediction based on the global description of rna sequence","volume":"47","author":"Tong","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2025062614063581000_ref17","doi-asserted-by":"crossref","article-title":"An empirical evaluation of doc2vec with practical insights into document embedding generation","author":"Lau","DOI":"10.18653\/v1\/W16-1609"},{"key":"2025062614063581000_ref16","doi-asserted-by":"publisher","first-page":"4372","DOI":"10.3390\/molecules25194372","article-title":"Lncmirnet: Predicting lncrna\u2013mirna interaction based on deep learning of ribonucleic acid sequences","volume":"25","author":"Yang","year":"2020","journal-title":"Molecules"},{"key":"2025062614063581000_ref18","doi-asserted-by":"publisher","first-page":"2401","DOI":"10.1109\/TKDE.2020.3006475","article-title":"Role-based graph embeddings","volume":"34","author":"Ahmed","year":"2020","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2025062614063581000_ref19","doi-asserted-by":"publisher","first-page":"2986","DOI":"10.1093\/bioinformatics\/btaa074","article-title":"Pmlipred: A method based on hybrid model and fuzzy decision for plant mirna\u2013lncrna interaction prediction","volume":"36","author":"Kang","year":"2020","journal-title":"Bioinformatics"},{"key":"2025062614063581000_ref20","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1007\/s12539-019-00351-w","article-title":"Plant mirna\u2013lncrna interaction prediction with the ensemble of cnn and indrnn","volume":"12","author":"Zhang","year":"2020","journal-title":"Interdisciplinary Sciences: Computational Life Sciences"},{"key":"2025062614063581000_ref21","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1007\/s12539-021-00434-7","article-title":"Ensemble deep learning based on multi-level information enhancement and greedy fuzzy decision for plant mirna\u2013lncrna interaction prediction","volume":"13","author":"Kang","year":"2021","journal-title":"Interdisciplinary Sciences: Computational Life Sciences"},{"key":"2025062614063581000_ref22","doi-asserted-by":"publisher","first-page":"bbac107","DOI":"10.1093\/bib\/bbac107","article-title":"Rnai-frid: Novel feature representation method with information enhancement and dimension reduction for rna\u2013rna interaction","volume":"23","author":"Kang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025062614063581000_ref23","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad317","article-title":"Sequence pre-training-based graph neural network for predicting lncrna-mirna associations","volume":"24","author":"Wang","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025062614063581000_ref24","first-page":"44","article-title":"Pmlihfm: Predicting plant mirna-lncrna interactions with hybrid feature mining network","volume":"15","author":"Chen","year":"2023","journal-title":"Interdisciplinary Sciences: Computational Life Sciences"},{"key":"2025062614063581000_ref25","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/s12539-021-00458-z","article-title":"Using network distance analysis to predict lncrna\u2013mirna interactions","volume":"13","author":"Zhang","year":"2021","journal-title":"Interdisciplinary sciences: computational life sciences"},{"key":"2025062614063581000_ref26","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1109\/TCBB.2022.3215151","article-title":"Islmi: Predicting lncrna-mirna interactions based on information injection and second-order graph convolution network","volume":"20","author":"Song","year":"2022","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2025062614063581000_ref27","doi-asserted-by":"publisher","first-page":"2785436","DOI":"10.1155\/2023\/2785436","article-title":"Sebglma: Semantic embedded bipartite graph network for predicting lncrna-mirna associations","volume":"2023","author":"Zhao","year":"2023","journal-title":"International Journal of Intelligent Systems"},{"key":"2025062614063581000_ref28","doi-asserted-by":"publisher","first-page":"37578","DOI":"10.1109\/ACCESS.2020.2974349","article-title":"Gnmflmi: Graph regularized nonnegative matrix factorization for predicting lncrna-mirna interactions","volume":"8","author":"Wang","year":"2020","journal-title":"Ieee Access"},{"key":"2025062614063581000_ref29","doi-asserted-by":"publisher","first-page":"1548","DOI":"10.1109\/TPAMI.2010.231","article-title":"Graph regularized nonnegative matrix factorization for data representation","volume":"33","author":"Cai","year":"2010","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2025062614063581000_ref30","doi-asserted-by":"publisher","first-page":"3840","DOI":"10.1109\/TCYB.2016.2585355","article-title":"Graph regularized non-negative low-rank matrix factorization for image clustering","volume":"47","author":"Li","year":"2016","journal-title":"IEEE transactions on cybernetics"},{"key":"2025062614063581000_ref31","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac495","article-title":"Predicting mirna-disease associations based on lncrna\u2013mirna interactions and graph convolution networks","volume":"24","author":"Wang","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025062614063581000_ref32","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.ymeth.2023.11.005","article-title":"Multimodal contrastive representation learning for drug-target binding affinity prediction","volume":"220","author":"Zhang","year":"2023","journal-title":"Methods"},{"key":"2025062614063581000_ref33","article-title":"Kan: Kolmogorov-Arnold networks","author":"Liu","year":"2024"},{"key":"2025062614063581000_ref34","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1016\/j.ygeno.2021.02.002","article-title":"Predicting lncrna\u2013mirna interactions based on interactome network and graphlet interaction","volume":"113","author":"Zhang","year":"2021","journal-title":"Genomics"},{"key":"2025062614063581000_ref35","doi-asserted-by":"publisher","first-page":"D916","DOI":"10.1093\/nar\/gkaa1087","volume":"49","author":"Frankish","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2025062614063581000_ref36","doi-asserted-by":"publisher","first-page":"D135","DOI":"10.1093\/nar\/gky1031","article-title":"Lncipedia 5: Towards a reference set of human long non-coding rnas","volume":"47","author":"Volders","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2025062614063581000_ref37","doi-asserted-by":"publisher","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":"2025062614063581000_ref38","doi-asserted-by":"crossref","first-page":"D210","DOI":"10.1093\/nar\/gkr1175","article-title":"Noncode v3. 0: Integrative annotation of long noncoding rnas","volume":"40","author":"Dechao","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2025062614063581000_ref39","doi-asserted-by":"publisher","first-page":"D276","DOI":"10.1093\/nar\/gkx1004","article-title":"lncrnasnp2: An updated database of functional snps and mutations in human and mouse lncrnas","volume":"46","author":"Miao","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2025062614063581000_ref40","doi-asserted-by":"publisher","first-page":"110492","DOI":"10.1016\/j.knosys.2023.110492","article-title":"Multi-view graph neural network with cascaded attention for lncrna-mirna interaction prediction","volume":"268","author":"Li","year":"2023","journal-title":"Knowledge-Based Systems"},{"key":"2025062614063581000_ref41","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.eswa.2016.09.040","article-title":"Collaborative filtering and deep learning based recommendation system for cold start items","volume":"69","author":"Wei","year":"2017","journal-title":"Expert Systems with Applications"},{"key":"2025062614063581000_ref42","article-title":"Visualizing data using t-sne","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"Journal of machine learning research"},{"key":"2025062614063581000_ref43","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"Journal of computational and applied mathematics"},{"key":"2025062614063581000_ref44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/03610927408827101","article-title":"A dendrite method for cluster analysis","volume":"3","author":"Cali\u0144ski","year":"1974","journal-title":"Communications in Statistics-theory and Methods"},{"key":"2025062614063581000_ref45","first-page":"6196","article-title":"Xist promote the proliferation and migration of non-small cell lung cancer cells via sponging mir-16 and regulating cdk8 expression","volume":"11","author":"Zhou","year":"2019","journal-title":"American journal of translational research"},{"key":"2025062614063581000_ref46","doi-asserted-by":"publisher","first-page":"11045","DOI":"10.2147\/OTT.S260499","article-title":"Hsa-mir-590-3p promotes the malignancy progression of pancreatic ductal carcinoma by inhibiting the expression of p27 and ppp2r2a via g1\/s cell cycle pathway","volume":"13","author":"Shi","year":"2020","journal-title":"Onco Targets Ther"},{"key":"2025062614063581000_ref47","doi-asserted-by":"publisher","first-page":"8","DOI":"10.4149\/neo_2018_170324N214","article-title":"Sox9, mir-495, mir-590-3p, and mir-320d were identified as chemoradiotherapy-sensitive genes and mirnas in colorectal cancer patients based on a microarray dataset","volume":"66","author":"Du","year":"2019","journal-title":"Neoplasma"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/3\/bbaf281\/63502263\/bbaf281.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/3\/bbaf281\/63502263\/bbaf281.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T18:06:42Z","timestamp":1750961202000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf281\/8164224"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,1]]},"references-count":47,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,5,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf281","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,5]]},"published":{"date-parts":[[2025,5,1]]},"article-number":"bbaf281"}}