{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T10:04:07Z","timestamp":1775556247029,"version":"3.50.1"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"21","license":[{"start":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T00:00:00Z","timestamp":1662940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31971371"],"award-info":[{"award-number":["31971371"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020C03010"],"award-info":[{"award-number":["2020C03010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Identifying genes that play a causal role in cancer evolution remains one of the biggest challenges in cancer biology. With the accumulation of high-throughput multi-omics data over decades, it becomes a great challenge to effectively integrate these data into the identification of cancer driver genes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we propose MODIG, a graph attention network (GAT)-based framework to identify cancer driver genes by combining multi-omics pan-cancer data (mutations, copy number variants, gene expression and methylation levels) with multi-dimensional gene networks. First, we established diverse types of gene relationship maps based on protein\u2013protein interactions, gene sequence similarity, KEGG pathway co-occurrence, gene co-expression patterns and gene ontology. Then, we constructed a multi-dimensional gene network consisting of approximately 20\u00a0000 genes as nodes and five types of gene associations as multiplex edges. We applied a GAT to model within-dimension interactions to generate a gene representation for each dimension based on this graph. Moreover, we introduced a joint learning module to fuse multiple dimension-specific representations to generate general gene representations. Finally, we used the obtained gene representation to perform a semi-supervised driver gene identification task. The experiment results show that MODIG outperforms the baseline models in terms of area under precision-recall curves and area under the receiver operating characteristic curves.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The MODIG program is available at https:\/\/github.com\/zjupgx\/modig. The code and data underlying this article are also available on Zenodo, at https:\/\/doi.org\/10.5281\/zenodo.7057241.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac622","type":"journal-article","created":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T15:53:09Z","timestamp":1662997989000},"page":"4901-4907","source":"Crossref","is-referenced-by-count":44,"title":["MODIG: integrating multi-omics and multi-dimensional gene network for cancer driver gene identification based on graph attention network model"],"prefix":"10.1093","volume":"38","author":[{"given":"Wenyi","family":"Zhao","sequence":"first","affiliation":[{"name":"Institute of Drug Metabolism and Pharmaceutical Analysis and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, China"},{"name":"Collaborative Innovation Center of Artificial Intelligence by MOE and Zhejiang Provincial Government, College of Computer Science and Technology, Zhejiang University , Hangzhou 310027, China"},{"name":"Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University , Hangzhou 310018, China"}]},{"given":"Xun","family":"Gu","sequence":"additional","affiliation":[{"name":"Department of Genetics, Development and Cell Biology, Iowa State University , Ames, IA 50011, USA"}]},{"given":"Shuqing","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Drug Metabolism and Pharmaceutical Analysis and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, China"}]},{"given":"Jian","family":"Wu","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center of Artificial Intelligence by MOE and Zhejiang Provincial Government, College of Computer Science and Technology, Zhejiang University , Hangzhou 310027, China"},{"name":"Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University , Hangzhou 310018, China"},{"name":"Second Affiliated Hospital School of Medicine, School of Public Health, Zhejiang University , Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2730-5483","authenticated-orcid":false,"given":"Zhan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Drug Metabolism and Pharmaceutical Analysis and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, China"},{"name":"Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University , Hangzhou 310018, China"},{"name":"Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare , Hangzhou 310058, China"}]}],"member":"286","published-online":{"date-parts":[[2022,9,12]]},"reference":[{"key":"2022103112472313500_btac622-B1","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.gendis.2018.12.003","article-title":"Regulation and functions of integrin \u03b12 in cell adhesion and disease","volume":"6","author":"Adorno-Cruz","year":"2019","journal-title":"Genes Dis"},{"key":"2022103112472313500_btac622-B2","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.cell.2018.02.060","article-title":"Comprehensive characterization of cancer driver genes and mutations","volume":"173","author":"Bailey","year":"2018","journal-title":"Cell"},{"key":"2022103112472313500_btac622-B3","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1038\/s41416-019-0648-6","article-title":"Principles and mechanisms of non-genetic resistance in cancer","volume":"122","author":"Bell","year":"2020","journal-title":"Br. 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