{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T22:26:38Z","timestamp":1781821598625,"version":"3.54.5"},"reference-count":54,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T00:00:00Z","timestamp":1620950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>CRISPR\/Cas9 is a powerful genome-editing technology that has been widely applied in targeted gene repair and gene expression regulation. One of the main challenges for the CRISPR\/Cas9 system is the occurrence of unexpected cleavage at some sites (off-targets) and predicting them is necessary due to its relevance in gene editing research. Very few deep learning models have been developed so far to predict the off-target propensity of single guide RNA (sgRNA) at specific DNA fragments by using artificial feature extract operations and machine learning techniques; however, this is a convoluted process that is difficult to understand and implement for researchers. In this research work, we introduce a novel graph-based approach to predict off-target efficacy of sgRNA in the CRISPR\/Cas9 system that is easy to understand and replicate for researchers. This is achieved by creating a graph with sequences as nodes and by using a link prediction method to predict the presence of links between sgRNA and off-target inducing target DNA sequences. Features for the sequences are extracted from within the sequences. We used HEK293 and K562 t datasets in our experiments. GCN predicted the off-target gene knockouts (using link prediction) by predicting the links between sgRNA and off-target sequences with an auROC value of 0.987.<\/jats:p>","DOI":"10.3390\/e23050608","type":"journal-article","created":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T10:54:22Z","timestamp":1620989662000},"page":"608","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Prediction of sgRNA Off-Target Activity in CRISPR\/Cas9 Gene Editing Using Graph Convolution Network"],"prefix":"10.3390","volume":"23","author":[{"given":"Prasoon Kumar","family":"Vinodkumar","sequence":"first","affiliation":[{"name":"iCV Lab, Institute of Technology, University of Tartu, 51009 Tartu, Estonia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cagri","family":"Ozcinar","sequence":"additional","affiliation":[{"name":"iCV Lab, Institute of Technology, University of Tartu, 51009 Tartu, Estonia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8460-5717","authenticated-orcid":false,"given":"Gholamreza","family":"Anbarjafari","sequence":"additional","affiliation":[{"name":"iCV Lab, Institute of Technology, University of Tartu, 51009 Tartu, Estonia"},{"name":"PwC Advisory Finland, 00180 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1146\/annurev-genet-110410-132430","article-title":"CRISPR-Cas systems in bacteria and archaea: Versatile small RNAs for adaptive defense and regulation","volume":"45","author":"Bhaya","year":"2011","journal-title":"Annu. 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