{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T23:14:23Z","timestamp":1787008463108,"version":"3.56.0"},"reference-count":68,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T00:00:00Z","timestamp":1690934400000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SoBigData++"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Disease gene prioritization consists in identifying genes that are likely to be involved in the mechanisms of a given disease, providing a ranking of such genes. Recently, the research community has used computational methods to uncover unknown gene\u2013disease associations; these methods range from combinatorial to machine learning-based approaches. In particular, during the last years, approaches based on deep learning have provided superior results compared to more traditional ones. Yet, the problem with these is their inherent black-box structure, which prevents interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose a new methodology for disease gene discovery, which leverages graph-structured data using graph neural networks (GNNs) along with an explainability phase for determining the ranking of candidate genes and understanding the model\u2019s output. Our approach is based on a positive\u2013unlabeled learning strategy, which outperforms existing gene discovery methods by exploiting GNNs in a non-black-box fashion. Our methodology is effective even in scenarios where a large number of associated genes need to be retrieved, in which gene prioritization methods often tend to lose their reliability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The source code of XGDAG is available on GitHub at: https:\/\/github.com\/GiDeCarlo\/XGDAG. The data underlying this article are available at: https:\/\/www.disgenet.org\/, https:\/\/thebiogrid.org\/, https:\/\/doi.org\/10.1371\/journal.pcbi.1004120.s003, and https:\/\/doi.org\/10.1371\/journal.pcbi.1004120.s004.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad482","type":"journal-article","created":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T12:48:43Z","timestamp":1690980523000},"source":"Crossref","is-referenced-by-count":26,"title":["XGDAG: explainable gene\u2013disease associations via graph neural networks"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3456-9428","authenticated-orcid":false,"given":"Andrea","family":"Mastropietro","sequence":"first","affiliation":[{"name":"Department of Computer, Control and Management Engineering \u201cAntonio Ruberti\u201d, Sapienza University of Rome , Rome 00185, 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