{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T22:19:06Z","timestamp":1784845146739,"version":"3.55.0"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"content-version":"vor","delay-in-days":30,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Innovation 2030 - Brain Science and Brain-inspired Artificial Intelligence Key Project","award":["2021ZD0202400"],"award-info":[{"award-number":["2021ZD0202400"]}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2022YFC3502500"],"award-info":[{"award-number":["2022YFC3502500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["L232103"],"award-info":[{"award-number":["L232103"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32100512"],"award-info":[{"award-number":["32100512"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010097","name":"China Association for Science and Technology","doi-asserted-by":"publisher","award":["2020QNRC001"],"award-info":[{"award-number":["2020QNRC001"]}],"id":[{"id":"10.13039\/100010097","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61806010"],"award-info":[{"award-number":["61806010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The interrelation and complementary nature of multi-omics data can provide valuable insights into the intricate molecular mechanisms underlying diseases. However, challenges such as limited sample size, high data dimensionality and differences in omics modalities pose significant obstacles to fully harnessing the potential of these data. The prior knowledge such as gene regulatory network and pathway information harbors useful gene\u2013gene interaction and gene functional module information. To effectively integrate multi-omics data and make full use of the prior knowledge, here, we propose a Multilevel-graph neural network (GNN): a hierarchically designed deep learning algorithm that sequentially leverages multi-omics data, gene regulatory networks and pathway information to extract features and enhance accuracy in predicting survival risk. Our method achieved better accuracy compared with existing methods. Furthermore, key factors nonlinearly associated with the tumor pathogenesis are prioritized by employing two interpretation algorithms (i.e. GNN-Explainer and IGscore) for neural networks, at gene and pathway level, respectively. The top genes and pathways exhibit strong associations with disease in survival analyses, many of which such as SEC61G and CYP27B1 are previously reported in the literature.<\/jats:p>","DOI":"10.1093\/bib\/bbae184","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T21:39:27Z","timestamp":1714167567000},"source":"Crossref","is-referenced-by-count":39,"title":["Prior knowledge-guided multilevel graph neural network for tumor risk prediction and interpretation via multi-omics data integration"],"prefix":"10.1093","volume":"25","author":[{"given":"Hongxi","family":"Yan","sequence":"first","affiliation":[{"name":"Department of Computer Science, Beihang University , XueYuan Road, 100191, BeiJing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dawei","family":"Weng","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Capital Medical University , 10 You An Men WaiXi Tou Tiao, 100069, Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongguo","family":"Li","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Capital Medical University , 10 You An Men WaiXi Tou Tiao, 100069, Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Capital Medical University , 10 You An Men WaiXi Tou Tiao, 100069, Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenji","family":"Ma","sequence":"additional","affiliation":[{"name":"Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine , 227 South Chongqing Road, 200025, Shanghai , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingjie","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Beihang University , XueYuan 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