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Most methods for inferring GRNs suffer from the inability to eliminate transitive interactions or necessitate expensive computational resources. To address these, we present a novel method, termed GMFGRN, for accurate graph neural network (GNN)-based GRN inference from scRNA-seq data. GMFGRN employs GNN for matrix factorization and learns representative embeddings for genes. For transcription factor\u2013gene pairs, it utilizes the learned embeddings to determine whether they interact with each other. The extensive suite of benchmarking experiments encompassing eight static scRNA-seq datasets alongside several state-of-the-art methods demonstrated mean improvements of 1.9 and 2.5% over the runner-up in area under the receiver operating characteristic curve (AUROC) and area under the precision\u2013recall curve (AUPRC). In addition, across four time-series datasets, maximum enhancements of 2.4 and 1.3% in AUROC and AUPRC were observed in comparison to the runner-up. Moreover, GMFGRN requires significantly less training time and memory consumption, with time and memory consumed &amp;lt;10% compared to the second-best method. These findings underscore the substantial potential of GMFGRN in the inference of GRNs. It is publicly available at https:\/\/github.com\/Lishuoyy\/GMFGRN.<\/jats:p>","DOI":"10.1093\/bib\/bbad529","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T16:49:47Z","timestamp":1706028587000},"source":"Crossref","is-referenced-by-count":30,"title":["GMFGRN: a matrix factorization and graph neural network approach for gene regulatory network inference"],"prefix":"10.1093","volume":"25","author":[{"given":"Shuo","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology , 200 Xiaolingwei, Nanjing, 210094 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5331-3655","authenticated-orcid":false,"given":"Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of information Engineering, Yangzhou University , 196 West Huayang, Yangzhou, 225000 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0045-4745","authenticated-orcid":false,"given":"Long-Chen","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology , 200 Xiaolingwei, Nanjing, 210094 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"He","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology , 200 Xiaolingwei, Nanjing, 210094 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8031-9086","authenticated-orcid":false,"given":"Jiangning","family":"Song","sequence":"additional","affiliation":[{"name":"Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University , Melbourne, Victoria 3800 , Australia"},{"name":"Monash Data Futures Institute, Monash University , Melbourne, Victoria 3800 , Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6786-8053","authenticated-orcid":false,"given":"Dong-Jun","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology , 200 Xiaolingwei, Nanjing, 210094 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,1,22]]},"reference":[{"key":"2024041211411992500_ref1","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.ydbio.2009.10.032","article-title":"Challenges for modeling global gene regulatory networks during development: insights from Drosophila","volume":"340","author":"Wilczynski","year":"2010","journal-title":"Dev 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