{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T07:52:09Z","timestamp":1778226729345,"version":"3.51.4"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T00:00:00Z","timestamp":1693440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"MBZUAI-WIS"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>MicroRNAs (miRNAs) silence genes by binding to messenger RNAs, whereas long non-coding RNAs (lncRNAs) act as competitive endogenous RNAs (ceRNAs) that can relieve miRNA silencing effects and upregulate target gene expression. The ceRNA association between lncRNAs and miRNAs has been a research hotspot due to its medical importance, but it is challenging to verify experimentally. In this paper, we propose a novel deep learning scheme, i.e. sequence pre-training-based graph neural network (SPGNN), that combines pre-training and fine-tuning stages to predict lncRNA\u2013miRNA associations from RNA sequences and the existing interactions represented as a graph. First, we utilize a sequence-to-vector technique to generate pre-trained embeddings based on the sequences of all RNAs during the pre-training stage. In the fine-tuning stage, we use Graph Neural Network to learn node representations from the heterogeneous graph constructed using lncRNA\u2013miRNA association information. We evaluate our proposed scheme SPGNN on our newly collected animal lncRNA\u2013miRNA association dataset and demonstrate that combining the $k$-mer technique and Doc2vec model for pre-training with the Simple Graph Convolution Network for fine-tuning is effective in predicting lncRNA\u2013miRNA associations. Our approach outperforms state-of-the-art baselines across various evaluation metrics. We also conduct an ablation study and hyperparameter analysis to verify the effectiveness of each component and parameter of our scheme. The complete code and dataset are available on GitHub: https:\/\/github.com\/zixwang\/SPGNN.<\/jats:p>","DOI":"10.1093\/bib\/bbad317","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T19:03:04Z","timestamp":1693508584000},"source":"Crossref","is-referenced-by-count":29,"title":["Sequence pre-training-based graph neural network for predicting lncRNA-miRNA associations"],"prefix":"10.1093","volume":"24","author":[{"given":"Zixiao","family":"Wang","sequence":"first","affiliation":[{"name":"Mohamed bin Zayed University of Artificial Intelligence , Masdar City, UAE"}]},{"given":"Shiyang","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Tangdu Hospital, Air Force Medical University , 569 Xinsi Road, Xi'an 710038, Shaanxi, China"}]},{"given":"Siwei","family":"Liu","sequence":"additional","affiliation":[{"name":"Mohamed bin Zayed University of Artificial Intelligence , Masdar City, UAE"}]},{"given":"Zhaohan","family":"Meng","sequence":"additional","affiliation":[{"name":"University of Glasgow"}]},{"given":"Jingjie","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Tangdu Hospital, Air Force Medical University , 569 Xinsi Road, Xi'an 710038, Shaanxi, China"}]},{"given":"Shangsong","family":"Liang","sequence":"additional","affiliation":[{"name":"Mohamed bin Zayed University of Artificial Intelligence , Masdar City, UAE"}]}],"member":"286","published-online":{"date-parts":[[2023,8,31]]},"reference":[{"issue":"7","key":"2023092216493783300_ref1","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1038\/nrg1379","article-title":"MicroRNAs: small RNAs with a big role in gene regulation","volume":"5","author":"He","year":"2004","journal-title":"Nat Rev Genet"},{"issue":"1","key":"2023092216493783300_ref2","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.cell.2018.03.006","article-title":"Metazoan microRNAs","volume":"173","author":"Bartel","year":"2018","journal-title":"Cell"},{"issue":"9","key":"2023092216493783300_ref3","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1038\/s41576-018-0017-y","article-title":"Towards a complete map of the human long non-coding RNA transcriptome","volume":"19","author":"Uszczynska-Ratajczak","year":"2018","journal-title":"Nat Rev Genet"},{"key":"2023092216493783300_ref4","doi-asserted-by":"crossref","first-page":"109006","DOI":"10.1016\/j.clim.2022.109006","article-title":"LncRNA SOX2OT facilitates LPS-induced inflammatory injury by regulating intercellular adhesion molecule 1 (ICAM1) via sponging miR-215-5p","volume":"238","author":"Zhu","year":"2022","journal-title":"Clin Immunol"},{"issue":"6","key":"2023092216493783300_ref5","doi-asserted-by":"crossref","first-page":"2702","DOI":"10.1002\/jmv.27578","article-title":"LncRNA LINC00924 upregulates NDRG2 to inhibit epithelial-mesenchymal transition via sponging miR-6755-5p in hepatitis B virus-related hepatocellular carcinoma","volume":"94","author":"Kai","year":"2022","journal-title":"J Med Virol"},{"key":"2023092216493783300_ref6","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2022.902329","article-title":"Analysis of characteristic genes and ceRNA regulation mechanism of endometriosis based on full transcriptional sequencing","volume":"13","author":"Xie","year":"2022","journal-title":"Front Genet"},{"key":"2023092216493783300_ref7","article-title":"Integrated analysis of lncRNA-miRNA-mRNA ceRNA network in mixed dry eye disease","volume":"2022","author":"Zhongxia Tang","year":"2022","journal-title":"Contrast Media Mol Imaging"},{"issue":"D1","key":"2023092216493783300_ref8","doi-asserted-by":"crossref","first-page":"D92","DOI":"10.1093\/nar\/gkt1248","article-title":"Starbase v2. 0: decoding miRNA-ceRNA, miRNA-ncRNA and protein\u2013RNA interaction networks from large-scale clip-seq data","volume":"42","author":"Li","year":"2014","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"2023092216493783300_ref9","doi-asserted-by":"crossref","first-page":"bbab440","DOI":"10.1093\/bib\/bbab440","article-title":"Mining plant endogenous target mimics from miRNA\u2013lncRNA interactions based on dual-path parallel ensemble pruning method","volume":"23","author":"Kang","year":"2022","journal-title":"Brief Bioinform"},{"issue":"3","key":"2023092216493783300_ref10","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.cell.2011.07.014","article-title":"A ceRNA hypothesis: the Rosetta stone of a hidden RNA language","volume":"146","author":"Salmena","year":"2011","journal-title":"Cell"},{"issue":"5","key":"2023092216493783300_ref11","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1093\/bioinformatics\/btx672","article-title":"Constructing prediction models from expression profiles for large scale lncRNA\u2013miRNA interaction profiling","volume":"34","author":"Huang","year":"2018","journal-title":"Bioinformatics"},{"issue":"11","key":"2023092216493783300_ref12","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.1093\/bioinformatics\/btp163","article-title":"Biopython: freely available python tools for computational molecular biology and bioinformatics","volume":"25","author":"Cock","year":"2009","journal-title":"Bioinformatics"},{"issue":"1","key":"2023092216493783300_ref13","doi-asserted-by":"crossref","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":"2023092216493783300_ref14","doi-asserted-by":"crossref","first-page":"103323","DOI":"10.1016\/j.jbi.2019.103323","article-title":"SECNLP: a survey of embeddings in clinical natural language processing","volume":"101","author":"Kalyan","year":"2020","journal-title":"J Biomed Inform"},{"key":"2023092216493783300_ref15","article-title":"Efficient estimation of word representations in vector space","volume-title":"In: International Conference on Learning Representations","author":"Mikolov","year":"2013"},{"key":"2023092216493783300_ref16","article-title":"Distributed representations of sentences and documents","volume-title":"In: International Conference on Machine Learning","author":"Le","year":"2014"},{"key":"2023092216493783300_ref17","first-page":"5998","article-title":"Attention is all you need","volume-title":"Advances in Neural Information Processing Systems","author":"Vaswani","year":"2017"},{"issue":"3","key":"2023092216493783300_ref18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3568953","article-title":"Graph neural pre-training for recommendation with side information","volume":"41","author":"Liu","year":"2023","journal-title":"ACM Trans Inf Syst"},{"key":"2023092216493783300_ref19","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Advances in Neural Information Processing Systems","author":"Hamilton","year":"2017"},{"key":"2023092216493783300_ref20","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"International Conference on Learning Representations","author":"Kipf","year":"2017"},{"key":"2023092216493783300_ref21","article-title":"How attentive are graph attention networks?","volume-title":"International Conference on Learning Representations","author":"Brody","year":"2022"},{"key":"2023092216493783300_ref22","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1145\/2623330.2623732","article-title":"Deepwalk: Online learning of social representations","volume-title":"Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Perozzi","year":"2014"},{"key":"2023092216493783300_ref23","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1145\/2939672.2939754","article-title":"node2vec: scalable feature learning for networks","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Grover","year":"2016"},{"key":"2023092216493783300_ref24","doi-asserted-by":"crossref","DOI":"10.1145\/2736277.2741093","article-title":"Line: large-scale information network embedding","volume-title":"Proceedings of the 24th International Conference on World Wide Web","author":"Tang","year":"2015"},{"key":"2023092216493783300_ref25","first-page":"2019","article-title":"Simplifying graph convolutional networks","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Chiang"},{"key":"2023092216493783300_ref26","article-title":"Graph attention networks","volume-title":"International Conference on Learning Representations","author":"Velickovic","year":"2018"},{"key":"2023092216493783300_ref27","first-page":"1144","article-title":"GNN-film: Graph neural networks with feature-wise linear modulation","volume-title":"International Conference on Machine Learning","author":"Brockschmidt","year":"2020"},{"key":"2023092216493783300_ref28","journal-title":"International Conference on Learning Representations"},{"issue":"5","key":"2023092216493783300_ref29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3326362","article-title":"Dynamic graph CNN for learning on point clouds","volume":"38","author":"Wang","year":"2019","journal-title":"ACM Trans Graphics"},{"key":"2023092216493783300_ref30","article-title":"Do we need anisotropic graph neural networks?","volume-title":"International Conference on Learning Representations","author":"Tailor","year":"2021"},{"issue":"D1","key":"2023092216493783300_ref31","doi-asserted-by":"crossref","first-page":"D183","DOI":"10.1093\/nar\/gkab1092","article-title":"LncACTdb 3.0: an updated database of experimentally supported ceRNA interactions and personalized networks contributing to precision medicine","volume":"50","author":"Wang","year":"2022","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2023092216493783300_ref32","doi-asserted-by":"crossref","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"},{"issue":"D1","key":"2023092216493783300_ref33","doi-asserted-by":"crossref","first-page":"D165","DOI":"10.1093\/nar\/gkaa1046","article-title":"Noncodev6: an updated database dedicated to long non-coding rna annotation in both animals and plants","volume":"49","author":"Zhao","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2023092216493783300_ref34","journal-title":"Proceedings of NAACL-HLT"},{"issue":"1","key":"2023092216493783300_ref35","doi-asserted-by":"crossref","first-page":"lqac012","DOI":"10.1093\/nargab\/lqac012","article-title":"Informative rna base embedding for rna structural alignment and clustering by deep representation learning","volume":"4","author":"Akiyama","year":"2022","journal-title":"NAR Genom Bioinform"},{"issue":"4","key":"2023092216493783300_ref36","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1145\/582415.582418","article-title":"Cumulated gain-based evaluation of IR techniques","volume":"20","author":"Jarvelin","year":"2002","journal-title":"ACM Trans Inf Syst"},{"key":"2023092216493783300_ref37","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.compbiolchem.2018.03.009","article-title":"Splicevec: distributed feature representations for splice junction prediction","volume":"74","author":"Dutta","year":"2018","journal-title":"Comput Biol Chem"},{"issue":"6","key":"2023092216493783300_ref38","doi-asserted-by":"crossref","first-page":"bbab351","DOI":"10.1093\/bib\/bbab351","article-title":"Leveraging the attention mechanism to improve the identification of DNA n6-methyladenine sites","volume":"22","author":"Zhang","year":"2021","journal-title":"Brief Bioinform"},{"issue":"Supplement_1","key":"2023092216493783300_ref39","doi-asserted-by":"crossref","first-page":"i308","DOI":"10.1093\/bioinformatics\/btab300","article-title":"Thermodynamic modeling reveals widespread multivalent binding by rna-binding proteins","volume":"37","author":"Sohrabi-Jahromi","year":"2021","journal-title":"Bioinformatics"},{"issue":"12","key":"2023092216493783300_ref40","first-page":"2515","article-title":"Sp1-induced up-regulation of lncRNA SNHG14 as a ceRNA promotes migration and invasion of clear cell renal cell carcinoma by regulating n-wasp","volume":"7","author":"Liu","year":"2017","journal-title":"Am J Cancer Res"},{"key":"2023092216493783300_ref41","doi-asserted-by":"crossref","first-page":"4865","DOI":"10.2147\/OTT.S244530","article-title":"Long non-coding RNA SNHG14 contributes to the development of hepatocellular carcinoma via sponging miR-217","volume":"13","author":"Xiaoyong","year":"2020","journal-title":"Onco Targets Ther"},{"key":"2023092216493783300_ref42","doi-asserted-by":"crossref","first-page":"110216","DOI":"10.1016\/j.cellsig.2021.110216","article-title":"LncRNA TUG1 promotes bladder cancer malignant behaviors by regulating the miR-320a\/FOXQ1 axis","volume":"91","author":"Tan","year":"2022","journal-title":"Cell Signal"},{"issue":"3","key":"2023092216493783300_ref43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3892\/etm.2022.11126","article-title":"Long non-coding RNA TUG1 knockdown repressed the viability, migration and differentiation of osteoblasts by sponging miR-214","volume":"23","author":"Yao","year":"2022","journal-title":"Exp Ther Med"},{"issue":"1","key":"2023092216493783300_ref44","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1080\/21655979.2021.2020393","article-title":"High expression of small nucleolar RNA host gene 3 predicts poor prognosis and promotes bone metastasis in prostate cancer by activating transforming growth factor-beta signaling","volume":"13","author":"Xi","year":"2022","journal-title":"Bioengineered"},{"key":"2023092216493783300_ref45","doi-asserted-by":"crossref","DOI":"10.1155\/2021\/9046798","article-title":"LINC00665 targets miR-214-3p\/MAPK1 axis to accelerate hepatocellular carcinoma growth and Warburg effect","volume":"2021","author":"Wan","year":"2021","journal-title":"J Oncol"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/5\/bbad317\/51710723\/bbad317.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/5\/bbad317\/51710723\/bbad317.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T17:04:20Z","timestamp":1695402260000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbad317\/7256790"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,31]]},"references-count":45,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,9,20]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbad317","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,9]]},"published":{"date-parts":[[2023,8,31]]},"article-number":"bbad317"}}