{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T00:51:55Z","timestamp":1782175915626,"version":"3.54.5"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T00:00:00Z","timestamp":1672358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"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":["61602309"],"award-info":[{"award-number":["61602309"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The inference of gene regulatory networks (GRNs) is of great importance for understanding the complex regulatory mechanisms within cells. The emergence of single-cell RNA-sequencing (scRNA-seq) technologies enables the measure of gene expression levels for individual cells, which promotes the reconstruction of GRNs at single-cell resolution. However, existing network inference methods are mainly designed for data collected from a single data source, which ignores the information provided by multiple related data sources. In this paper, we propose a multi-view contrastive learning (DeepMCL) model to infer GRNs from scRNA-seq data collected from multiple data sources or time points. We first represent each gene pair as a set of histogram images, and then introduce a deep Siamese convolutional neural network with contrastive loss to learn the low-dimensional embedding for each gene pair. Moreover, an attention mechanism is introduced to integrate the embeddings extracted from different data sources and different neighbor gene pairs. Experimental results on synthetic and real-world datasets validate the effectiveness of our contrastive learning and attention mechanisms, demonstrating the effectiveness of our model in integrating multiple data sources for GRN inference.<\/jats:p>","DOI":"10.1093\/bib\/bbac586","type":"journal-article","created":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T06:22:15Z","timestamp":1672467735000},"source":"Crossref","is-referenced-by-count":23,"title":["Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning"],"prefix":"10.1093","volume":"24","author":[{"given":"Zerun","family":"Lin","sequence":"first","affiliation":[{"name":"Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Media Security, and Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ), College of Electronics and Information Engineering, , Shenzhen, 518060 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Le","family":"Ou-Yang","sequence":"additional","affiliation":[{"name":"Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Media Security, and Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ), College of Electronics and Information Engineering, , Shenzhen, 518060 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,12,30]]},"reference":[{"issue":"1","key":"2023011917115468400_ref1","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/nrg2918","article-title":"Network medicine: a network-based approach to human disease","volume":"12","author":"Barab\u00e1si","year":"2011","journal-title":"Nat Rev Genet"},{"issue":"8","key":"2023011917115468400_ref2","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1038\/nmeth.2016","article-title":"Wisdom of crowds for 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