{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T16:49:57Z","timestamp":1775062197295,"version":"3.50.1"},"reference-count":56,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,4,19]],"date-time":"2025-04-19T00:00:00Z","timestamp":1745020800000},"content-version":"vor","delay-in-days":49,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Scientific and Technological Research Program of Chongqing Education Committee","award":["KJQN202300627"],"award-info":[{"award-number":["KJQN202300627"]}]},{"name":"Chongqing Eaglet Plan Project","award":["CY240606"],"award-info":[{"award-number":["CY240606"]}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"publisher","award":["CSTB2024NSCQ-KJFZMSX0036"],"award-info":[{"award-number":["CSTB2024NSCQ-KJFZMSX0036"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Accurate prediction of pathogenic variants in human disease-associated genes would have a profound effect on clinical decision-making; however, it remains a significant challenge due to the overwhelming number of these variants. We propose graph neural network for multimodal annotation-based pathogenicity prediction (GNN-MAP), a novel deep learning framework that effectively integrates multimodal annotations and similarity relationships among variants to predict the pathogenicity of multi-type variants. Trained on the ClinVar dataset, GNN-MAP exhibits superior predictive performance in internal validation and orthogonal test datasets, accurately predicting variant pathogenicity. Notably, GNN-MAP enables accurate prediction of the pathogenicity of rare variants and highly imbalanced datasets. Furthermore, it achieves high performance in the pathogenicity prediction of inherited retinal disease-specific variants, highlighting its effectiveness in disease-specific variant prediction. These findings suggest that the robust capability of GNN-MAP to predict pathogenicity across multiple variant types and datasets holds significant potential for applications in research and clinical settings.<\/jats:p>","DOI":"10.1093\/bib\/bbaf151","type":"journal-article","created":{"date-parts":[[2025,4,19]],"date-time":"2025-04-19T04:42:41Z","timestamp":1745037761000},"source":"Crossref","is-referenced-by-count":6,"title":["A graph neural network approach for accurate prediction of pathogenicity in multi-type variants"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4967-3515","authenticated-orcid":false,"given":"Hongtao","family":"Yu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications , No. 2 Chongwen Road, Nan'an District, Chongqing 400065 ,","place":["China"]},{"name":"Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications , No. 2 Chongwen Road, Nan'an District, Chongqing 400065 ,","place":["China"]}]},{"given":"Guojing","family":"He","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications , No. 2 Chongwen Road, Nan'an District, Chongqing 400065 ,","place":["China"]},{"name":"Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications , No. 2 Chongwen Road, Nan'an District, Chongqing 400065 ,","place":["China"]}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications , No. 2 Chongwen Road, Nan'an District, Chongqing 400065 ,","place":["China"]},{"name":"Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications , No. 2 Chongwen Road, Nan'an District, Chongqing 400065 ,","place":["China"]}]},{"given":"Senbiao","family":"Qin","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications , No. 2 Chongwen Road, Nan'an District, Chongqing 400065 ,","place":["China"]}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications , No. 2 Chongwen Road, Nan'an District, Chongqing 400065 ,","place":["China"]}]},{"given":"Mingze","family":"Bai","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications , No. 2 Chongwen Road, Nan'an District, Chongqing 400065 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