{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T16:29:58Z","timestamp":1780849798573,"version":"3.54.1"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T00:00:00Z","timestamp":1707782400000},"content-version":"vor","delay-in-days":22,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1R01 CA223667"],"award-info":[{"award-number":["1R01 CA223667"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01CA227713"],"award-info":[{"award-number":["R01CA227713"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Faculty Research Award from Google Inc"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, leading to inaccurate gene counts. Existing methods, including advanced deep learning techniques, struggle to reliably impute gene expressions due to a lack of mechanisms that explicitly consider the underlying biological knowledge of the system. In reality, it has long been recognized that gene\u2013gene interactions may serve as reflective indicators of underlying biology processes, presenting discriminative signatures of the cells. A genomic data analysis framework that is capable of leveraging the underlying gene\u2013gene interactions is thus highly desirable and could allow for more reliable identification of distinctive patterns of the genomic data through extraction and integration of intricate biological characteristics of the genomic data. Here we tackle the problem in two steps to exploit the gene\u2013gene interactions of the system. We first reposition the genes into a 2D grid such that their spatial configuration reflects their interactive relationships. To alleviate the need for labeled ground truth gene expression datasets, a self-supervised 2D convolutional neural network is employed to extract the contextual features of the interactions from the spatially configured genes and impute the omitted values. Extensive experiments with both simulated and experimental scRNA-seq datasets are carried out to demonstrate the superior performance of the proposed strategy against the existing imputation methods.<\/jats:p>","DOI":"10.1093\/bib\/bbae031","type":"journal-article","created":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T13:40:14Z","timestamp":1707831614000},"source":"Crossref","is-referenced-by-count":5,"title":["Self-supervised deep learning of gene\u2013gene interactions for improved gene expression recovery"],"prefix":"10.1093","volume":"25","author":[{"given":"Qingyue","family":"Wei","sequence":"first","affiliation":[{"name":"Institute for Computational and Mathematical Engineering, Stanford University , Stanford, 94305 CA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Tauhidul","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Stanford University , Stanford, 94305 CA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of California, Santa Cruz , Santa Cruz, 95064 CA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Stanford University , Stanford, 94305 CA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,2,12]]},"reference":[{"issue":"3","key":"2024021313254179100_ref1","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1038\/nmeth.4150","article-title":"Single-cell mRNA quantification and differential analysis with census","volume":"14","author":"Qiu","year":"2017","journal-title":"Nat 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