{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:44:18Z","timestamp":1780638258403,"version":"3.54.1"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000},"content-version":"vor","delay-in-days":18,"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":["62173235"],"award-info":[{"award-number":["62173235"]}],"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":["62473266"],"award-info":[{"award-number":["62473266"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"publisher","award":["2024B1515020059"],"award-info":[{"award-number":["2024B1515020059"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology Program","award":["RCYX20221008092922051"],"award-info":[{"award-number":["RCYX20221008092922051"]}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20230808105802006"],"award-info":[{"award-number":["JCYJ20230808105802006"]}]},{"DOI":"10.13039\/501100010226","name":"Department of Education of Guangdong Province","doi-asserted-by":"publisher","award":["2022ZDZX1022"],"award-info":[{"award-number":["2022ZDZX1022"]}],"id":[{"id":"10.13039\/501100010226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Gene regulatory networks (GRNs) unveil the intricate interactions among genes, pivotal in elucidating the complex biological processes within cells. The advent of single-cell RNA-sequencing (scRNA-seq) enables the inference of GRNs at single-cell resolution. However, the majority of current supervised network inference methods typically concentrate on predicting pairwise gene regulatory interaction, thus failing to fully exploit correlations among all genes and exhibiting limited generalization performance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To address these issues, we propose a graph contrastive link prediction (GCLink) model to infer potential gene regulatory interactions from scRNA-seq data. Based on known gene regulatory interactions and scRNA-seq data, GCLink introduces a graph contrastive learning strategy to aggregate the feature and neighborhood information of genes to learn their representations. This approach reduces the dependence of our model on sample size and enhance its ability in predicting potential gene regulatory interactions. Extensive experiments on real scRNA-seq datasets demonstrate that GCLink outperforms other state-of-the-art methods in most cases. Furthermore, by pretraining GCLink on a source cell line with abundant known regulatory interactions and fine-tuning it on a target cell line with limited amount of known interactions, our GCLink model exhibits good performance in GRN inference, demonstrating its effectiveness in inferring GRNs from datasets with limited known interactions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code and data are available at https:\/\/github.com\/Yoyiming\/GCLink.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf074","type":"journal-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T18:49:31Z","timestamp":1739818171000},"source":"Crossref","is-referenced-by-count":12,"title":["GCLink: a graph contrastive link prediction framework for gene regulatory network inference"],"prefix":"10.1093","volume":"41","author":[{"given":"Weiming","family":"Yu","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, College of Electronics and Information Engineering, Shenzhen University , Shenzhen 518060,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zerun","family":"Lin","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, College of Electronics and Information Engineering, Shenzhen University , Shenzhen 518060,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miaofang","family":"Lan","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, College of Electronics and Information Engineering, Shenzhen University , Shenzhen 518060,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4007-4568","authenticated-orcid":false,"given":"Le","family":"Ou-Yang","sequence":"additional","affiliation":[{"name":"Guangdong Laboratory of Machine Perception and Intelligent Computing, Faculty of Engineering, Shenzhen MSU-BIT University , Shenzhen 518116,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,2,17]]},"reference":[{"key":"2025030711564461700_btaf074-B1","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1039\/C4MB00413B","article-title":"CN: a consensus algorithm for inferring gene regulatory networks using the SORDER algorithm and conditional mutual information test","volume":"11","author":"Aghdam","year":"2015","journal-title":"Mol Biosyst"},{"key":"2025030711564461700_btaf074-B2","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/nature22796","article-title":"Multilineage communication regulates human liver bud development from pluripotency","volume":"546","author":"Camp","year":"2017","journal-title":"Nature"},{"key":"2025030711564461700_btaf074-B3","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.cels.2017.08.014","article-title":"Gene regulatory network inference from single-cell data using multivariate information measures","volume":"5","author":"Chan","year":"2017","journal-title":"Cell Syst"},{"key":"2025030711564461700_btaf074-B4","doi-asserted-by":"crossref","first-page":"4522","DOI":"10.1093\/bioinformatics\/btac559","article-title":"Graph attention network for link prediction of gene regulations from single-cell RNA-sequencing data","volume":"38","author":"Chen","year":"2022","journal-title":"Bioinformatics"},{"key":"2025030711564461700_btaf074-B5","doi-asserted-by":"crossref","first-page":"bbab325","DOI":"10.1093\/bib\/bbab325","article-title":"DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data","volume":"22","author":"Chen","year":"2021","journal-title":"Brief Bioinform"},{"key":"2025030711564461700_btaf074-B6","doi-asserted-by":"crossref","first-page":"bbae143","DOI":"10.1093\/bib\/bbae143","article-title":"DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding","volume":"25","author":"Gao","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025030711564461700_btaf074-B7","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1101\/gr.240663.118","article-title":"Benchmark and integration of resources for the estimation of human transcription factor activities","volume":"29","author":"Garcia-Alonso","year":"2019","journal-title":"Genome Res"},{"key":"2025030711564461700_btaf074-B8","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1038\/s41467-018-02866-0","article-title":"Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs","volume":"9","author":"Hayashi","year":"2018","journal-title":"Nat Commun"},{"key":"2025030711564461700_btaf074-B9","doi-asserted-by":"crossref","first-page":"D46","DOI":"10.1093\/nar\/gkac1067","article-title":"ChIPBase v3. 0: the encyclopedia of transcriptional regulations of non-coding RNAs and protein-coding genes","volume":"51","author":"Huang","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2025030711564461700_btaf074-B10","doi-asserted-by":"crossref","first-page":"e12776","DOI":"10.1371\/journal.pone.0012776","article-title":"Inferring regulatory networks from expression data using tree-based methods","volume":"5","author":"Huynh-Thu","year":"2010","journal-title":"PLoS One"},{"key":"2025030711564461700_btaf074-B11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s12276-018-0071-8","article-title":"Single-cell RNA sequencing technologies and bioinformatics pipelines","volume":"50","author":"Hwang","year":"2018","journal-title":"Exp Mol Med"},{"key":"2025030711564461700_btaf074-B12","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1186\/s12918-019-0694-y","article-title":"GNE: a deep learning framework for gene network inference by aggregating biological information","volume":"13","author":"Kc","year":"2019","journal-title":"BMC Syst Biol"},{"key":"2025030711564461700_btaf074-B13","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1038\/nmeth.2967","article-title":"Bayesian approach to single-cell differential expression analysis","volume":"11","author":"Kharchenko","year":"2014","journal-title":"Nat Methods"},{"key":"2025030711564461700_btaf074-B14","author":"Kipf"},{"key":"2025030711564461700_btaf074-B15","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.molcel.2015.04.005","article-title":"The technology and biology of single-cell RNA sequencing","volume":"58","author":"Kolodziejczyk","year":"2015","journal-title":"Mol Cell"},{"key":"2025030711564461700_btaf074-B16","doi-asserted-by":"crossref","first-page":"bbae334","DOI":"10.1093\/bib\/bbae334","article-title":"DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations","volume":"25","author":"Lei","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025030711564461700_btaf074-B17","doi-asserted-by":"crossref","first-page":"bbac586","DOI":"10.1093\/bib\/bbac586","article-title":"Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning","volume":"24","author":"Lin","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025030711564461700_btaf074-B18","doi-asserted-by":"crossref","first-page":"7605","DOI":"10.1038\/s41598-021-87074-5","article-title":"An order independent algorithm for inferring gene regulatory network using quantile value for conditional independence tests","volume":"11","author":"Mahmoodi","year":"2021","journal-title":"Sci Rep"},{"key":"2025030711564461700_btaf074-B19","doi-asserted-by":"crossref","first-page":"btad072","DOI":"10.1093\/bioinformatics\/btad072","article-title":"wpLogicNet: logic gate and structure inference in gene regulatory networks","volume":"39","author":"Malekpour","year":"2023","journal-title":"Bioinformatics"},{"key":"2025030711564461700_btaf074-B20","doi-asserted-by":"crossref","first-page":"bbad414","DOI":"10.1093\/bib\/bbad414","article-title":"Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks","volume":"24","author":"Mao","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025030711564461700_btaf074-B21","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1038\/nmeth.2016","article-title":"Wisdom of crowds for robust gene network inference","volume":"9","author":"Marbach","year":"2012","journal-title":"Nat Methods"},{"key":"2025030711564461700_btaf074-B22","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1093\/bioinformatics\/bty916","article-title":"GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks","volume":"35","author":"Moerman","year":"2019","journal-title":"Bioinformatics"},{"key":"2025030711564461700_btaf074-B23","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1038\/s41586-020-2493-4","article-title":"Expanded encyclopaedias of DNA elements in the human and mouse genomes","volume":"583","author":"Moore","year":"2020","journal-title":"Nature"},{"key":"2025030711564461700_btaf074-B24","first-page":"e20","article-title":"A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation","volume":"128","author":"Nestorowa","year":"2016","journal-title":"Blood J Am Soc Hematol"},{"key":"2025030711564461700_btaf074-B25","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1038\/s41592-019-0690-6","article-title":"Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data","volume":"17","author":"Pratapa","year":"2020","journal-title":"Nat Methods"},{"key":"2025030711564461700_btaf074-B26","doi-asserted-by":"crossref","first-page":"baw100","DOI":"10.1093\/database\/baw100","article-title":"The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins","volume":"2016","author":"Rouillard","year":"2016","journal-title":"Database"},{"key":"2025030711564461700_btaf074-B27","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1038\/nature13437","article-title":"Single-cell RNA-seq reveals dynamic paracrine control of cellular variation","volume":"510","author":"Shalek","year":"2014","journal-title":"Nature"},{"key":"2025030711564461700_btaf074-B28","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1038\/s43588-021-00099-8","article-title":"Modeling gene regulatory networks using neural network architectures","volume":"1","author":"Shu","year":"2021","journal-title":"Nat Comput Sci"},{"key":"2025030711564461700_btaf074-B29","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1093\/bioinformatics\/btw729","article-title":"Leap: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering","volume":"33","author":"Specht","year":"2017","journal-title":"Bioinformatics"},{"key":"2025030711564461700_btaf074-B30","doi-asserted-by":"crossref","first-page":"D607","DOI":"10.1093\/nar\/gky1131","article-title":"String v11: protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets","volume":"47","author":"Szklarczyk","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2025030711564461700_btaf074-B31","first-page":"10","article-title":"Graph attention networks","volume":"1050","author":"Velickovic","year":"2017","journal-title":"Stat"},{"key":"2025030711564461700_btaf074-B32","doi-asserted-by":"crossref","first-page":"e1010942","DOI":"10.1371\/journal.pgen.1010942","article-title":"Inferring gene regulatory network from single-cell transcriptomes with graph autoencoder model","volume":"19","author":"Wang","year":"2023","journal-title":"PLoS Genet"},{"key":"2025030711564461700_btaf074-B33","doi-asserted-by":"crossref","first-page":"btad165","DOI":"10.1093\/bioinformatics\/btad165","article-title":"STGRNs: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data","volume":"39","author":"Xu","year":"2023","journal-title":"Bioinformatics"},{"key":"2025030711564461700_btaf074-B34","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"You","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"2025030711564461700_btaf074-B35","doi-asserted-by":"crossref","first-page":"27151","DOI":"10.1073\/pnas.1911536116","article-title":"Deep learning for inferring gene relationships from single-cell expression data","volume":"116","author":"Yuan","year":"2019","journal-title":"Proc Natl Acad Sci USA"},{"key":"2025030711564461700_btaf074-B36","article-title":"Link prediction based on graph neural networks","author":"Zhang","year":"2018","journal-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems"},{"key":"2025030711564461700_btaf074-B37","doi-asserted-by":"crossref","first-page":"bbab009","DOI":"10.1093\/bib\/bbab009","article-title":"A comprehensive overview and critical evaluation of gene regulatory network inference technologies","volume":"22","author":"Zhao","year":"2021","journal-title":"Brief Bioinform"},{"key":"2025030711564461700_btaf074-B38","author":"Zhu"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaf074\/61932825\/btaf074.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/3\/btaf074\/62327826\/btaf074.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/3\/btaf074\/62327826\/btaf074.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T11:58:10Z","timestamp":1741348690000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btaf074\/8019657"}},"subtitle":[],"editor":[{"given":"Laura","family":"Cantini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,2,17]]},"references-count":38,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3,4]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaf074","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,3]]},"published":{"date-parts":[[2025,2,17]]},"article-number":"btaf074"}}