{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T10:32:35Z","timestamp":1770892355863,"version":"3.50.1"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T00:00:00Z","timestamp":1634601600000},"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":["61972251"],"award-info":[{"award-number":["61972251"]}],"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":["62073219"],"award-info":[{"award-number":["62073219"]}],"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":["61725302"],"award-info":[{"award-number":["61725302"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Reverse engineering of gene regulatory networks (GRNs) has long been an attractive research topic in system biology. Computational prediction of gene regulatory interactions has remained a challenging problem due to the complexity of gene expression and scarce information resources. The high-throughput spatial gene expression data, like in situ hybridization images that exhibit temporal and spatial expression patterns, has provided abundant and reliable information for the inference of GRNs. However, computational tools for analyzing the spatial gene expression data are highly underdeveloped.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we develop a new method for identifying gene regulatory interactions from gene expression images, called ConGRI. The method is featured by a contrastive learning scheme and deep Siamese convolutional neural network architecture, which automatically learns high-level feature embeddings for the expression images and then feeds the embeddings to an artificial neural network to determine whether or not the interaction exists. We apply the method to a Drosophila embryogenesis dataset and identify GRNs of eye development and mesoderm development. Experimental results show that ConGRI outperforms previous traditional and deep learning methods by a large margin, which achieves accuracies of 76.7% and 68.7% for the GRNs of early eye development and mesoderm development, respectively. It also reveals some master regulators for Drosophila eye development.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availabilityand implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/lugimzheng\/ConGRI.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab718","type":"journal-article","created":{"date-parts":[[2021,10,16]],"date-time":"2021-10-16T10:33:59Z","timestamp":1634380439000},"page":"746-753","source":"Crossref","is-referenced-by-count":25,"title":["Accurate inference of gene regulatory interactions from spatial gene expression with deep contrastive learning"],"prefix":"10.1093","volume":"38","author":[{"given":"Lujing","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University , Shanghai 200240, China"},{"name":"SJTU Paris Elite Institute of Technology (SPEIT), Shanghai Jiao Tong University , Shanghai 200240, China"}]},{"given":"Zhenhuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Automation, Shanghai Jiao Tong University , Shanghai 200240, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5720-773X","authenticated-orcid":false,"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University , Shanghai 200240, China"},{"name":"Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering , Shanghai 200240, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4029-3325","authenticated-orcid":false,"given":"Hong-Bin","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Automation, Shanghai Jiao Tong University , Shanghai 200240, China"},{"name":"Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University , Shanghai 200240, China"}]}],"member":"286","published-online":{"date-parts":[[2021,10,19]]},"reference":[{"key":"2023020108502846700_btab718-B1","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.jtbi.2006.10.027","article-title":"Robustness and evolvability in genetic regulatory networks","volume":"245","author":"Aldana","year":"2007","journal-title":"J. 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