{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T12:49:32Z","timestamp":1782910172930,"version":"3.54.5"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T00:00:00Z","timestamp":1718236800000},"content-version":"vor","delay-in-days":12,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFA0910700"],"award-info":[{"award-number":["2021YFA0910700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology","award":["GXWD20201230155427003-20200821222112001"],"award-info":[{"award-number":["GXWD20201230155427003-20200821222112001"]}]},{"name":"Shenzhen Science and Technology","award":["JCYJ20200109113201726"],"award-info":[{"award-number":["JCYJ20200109113201726"]}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"publisher","award":["2021A1515012461"],"award-info":[{"award-number":["2021A1515012461"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"publisher","award":["2021A1515220115"],"award-info":[{"award-number":["2021A1515220115"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies","award":["2022B1212010005"],"award-info":[{"award-number":["2022B1212010005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Accurately predicting the driver genes of cancer is of great significance for carcinogenesis progress research and cancer treatment. In recent years, more and more deep-learning-based methods have been used for predicting cancer driver genes. However, deep-learning algorithms often have black box properties and cannot interpret the output results. Here, we propose a novel cancer driver gene mining method based on heterogeneous network meta-paths (MCDHGN), which uses meta-path aggregation to enhance the interpretability of predictions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>MCDHGN constructs a heterogeneous network by using several types of multi-omics data that are biologically linked to genes. And the differential probabilities of SNV, DNA methylation, and gene expression data between cancerous tissues and normal tissues are extracted as initial features of genes. Nine meta-paths are manually selected, and the representation vectors obtained by aggregating information within and across meta-path nodes are used as new features for subsequent classification and prediction tasks. By comparing with eight homogeneous and heterogeneous network models on two pan-cancer datasets, MCDHGN has better performance on AUC and AUPR values. Additionally, MCDHGN provides interpretability of predicted cancer driver genes through the varying weights of biologically meaningful meta-paths.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/1160300611\/MCDHGN<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae362","type":"journal-article","created":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T10:26:04Z","timestamp":1718274364000},"source":"Crossref","is-referenced-by-count":8,"title":["MCDHGN: heterogeneous network-based cancer driver gene prediction and interpretability analysis"],"prefix":"10.1093","volume":"40","author":[{"given":"Lexiang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen) , Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingli","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen) , Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen) , Shenzhen 518055, China"},{"name":"Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Harbin Institute of Technology (Shenzhen) , Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6500-6217","authenticated-orcid":false,"given":"Yadong","family":"Wang","sequence":"additional","affiliation":[{"name":"Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology , Harbin, Heilongjiang 150001, China"},{"name":"Ministry of Education, Key Laboratory of Biological Bigdata, Harbin Institute of Technology , Harbin, Heilongjiang 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8045-5264","authenticated-orcid":false,"given":"Junyi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen) , Shenzhen 518055, China"},{"name":"Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Harbin Institute of Technology (Shenzhen) , Shenzhen 518055, China"},{"name":"Ministry of Education, Key Laboratory of Biological Bigdata, Harbin Institute of Technology , Harbin, Heilongjiang 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,6,12]]},"reference":[{"key":"2024071814105466200_btae362-B1","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1038\/s41586-020-1969-6","article-title":"Pan-cancer analysis of whole genomes","volume":"578","author":"ICGC\/TCGA Pan-Cancer Analysis of Whole Genomes Consortium","year":"2020","journal-title":"Nature"},{"key":"2024071814105466200_btae362-B2","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.semcancer.2019.02.001","article-title":"Mechanisms of PTEN loss in cancer: it\u2019s all about diversity","volume":"59","author":"\u00c1lvarez-Garcia","year":"2019","journal-title":"Semin Cancer Biol"},{"key":"2024071814105466200_btae362-B3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1200\/PO.17.00011","article-title":"Oncokb: a precision oncology knowledge base","volume":"1","author":"Chakravarty","year":"2017","journal-title":"JCO Precis Oncol"},{"key":"2024071814105466200_btae362-B4","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1038\/s41588-019-0572-y","article-title":"Identification of cancer driver genes based on nucleotide context","volume":"52","author":"Dietlein","year":"2020","journal-title":"Nat Genet"},{"key":"2024071814105466200_btae362-B5","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.cell.2018.03.033","article-title":"Perspective on oncogenic processes at the end of the beginning of cancer genomics","volume":"173","author":"Ding","year":"2018","journal-title":"Cell"},{"key":"2024071814105466200_btae362-B6","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13059-022-02607-z","article-title":"Comparative assessment of genes driving cancer and somatic evolution in non-cancer tissues: an update of the network of cancer genes (NCG) resource","volume":"23","author":"Dressler","year":"2022","journal-title":"Genome Biol"},{"key":"2024071814105466200_btae362-B7","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1038\/s41586-021-03922-4","article-title":"Biologically informed deep neural network for prostate cancer discovery","volume":"598","author":"Elmarakeby","year":"2021","journal-title":"Nature"},{"key":"2024071814105466200_btae362-B8","first-page":"2331","author":"Fu","year":"2020"},{"key":"2024071814105466200_btae362-B9","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1002\/dvdy.24485","article-title":"Pan-cancer survey of epithelial\u2013mesenchymal transition markers across the cancer genome atlas","volume":"247","author":"Gibbons","year":"2018","journal-title":"Dev Dynam"},{"key":"2024071814105466200_btae362-B10","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1158\/1078-0432.CCR-15-2549","article-title":"ESR1 mutations in breast cancer: proof-of-concept challenges clinical action","volume":"22","author":"Gu","year":"2016","journal-title":"Clin Cancer Res"},{"key":"2024071814105466200_btae362-B11","doi-asserted-by":"crossref","first-page":"e45","DOI":"10.1093\/nar\/gkz096","article-title":"DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies","volume":"47","author":"Han","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2024071814105466200_btae362-B12","first-page":"2704","author":"Hu","year":"2020"},{"key":"2024071814105466200_btae362-B13","doi-asserted-by":"crossref","first-page":"6001","DOI":"10.1158\/1078-0432.CCR-07-0071","article-title":"De novo identification of MIZ-1 (ZBTB17) encoding a MYC-interacting zinc-finger protein as a new favorable neuroblastoma gene","volume":"13","author":"Ikegaki","year":"2007","journal-title":"Clin Cancer Res"},{"key":"2024071814105466200_btae362-B14","doi-asserted-by":"crossref","first-page":"D712","DOI":"10.1093\/nar\/gkq1156","article-title":"ConsensusPathDB: toward a more complete picture of cell biology","volume":"39","author":"Kamburov","year":"2011","journal-title":"Nucleic Acids Res"},{"key":"2024071814105466200_btae362-B15","author":"Kipf","year":"2016"},{"key":"2024071814105466200_btae362-B16","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1038\/nature12213","article-title":"Mutational heterogeneity in cancer and the search for new cancer-associated genes","volume":"499","author":"Lawrence","year":"2013","journal-title":"Nature"},{"key":"2024071814105466200_btae362-B17","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1038\/s41592-019-0422-y","article-title":"Cancermine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer","volume":"16","author":"Lever","year":"2019","journal-title":"Nat Methods"},{"key":"2024071814105466200_btae362-B18","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.cels.2015.12.004","article-title":"The molecular signatures database hallmark gene set collection","volume":"1","author":"Liberzon","year":"2015","journal-title":"Cell Syst"},{"key":"2024071814105466200_btae362-B19","doi-asserted-by":"crossref","first-page":"bbab432","DOI":"10.1093\/bib\/bbab432","article-title":"Improving cancer driver gene identification using multi-task learning on graph convolutional network","volume":"23","author":"Peng","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024071814105466200_btae362-B20","doi-asserted-by":"crossref","first-page":"3430","DOI":"10.1109\/TNSE.2024.3373652","article-title":"Multi-network graph contrastive learning for cancer driver gene identification","volume":"11","author":"Peng","year":"2024","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"2024071814105466200_btae362-B21","doi-asserted-by":"crossref","first-page":"6663","DOI":"10.3390\/ijms21186663","article-title":"MuRF1\/TRIM63, master regulator of muscle mass","volume":"21","author":"Peris-Moreno","year":"2020","journal-title":"Int J Mol Sci"},{"key":"2024071814105466200_btae362-B22","doi-asserted-by":"crossref","first-page":"2157","DOI":"10.1038\/sj.onc.1210302","article-title":"Tp53 mutations in human cancers: functional selection and impact on cancer prognosis and outcomes","volume":"26","author":"Petitjean","year":"2007","journal-title":"Oncogene"},{"key":"2024071814105466200_btae362-B23","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.3390\/s21061938","article-title":"Gated graph attention network for cancer prediction","volume":"21","author":"Qiu","year":"2021","journal-title":"Sensors"},{"key":"2024071814105466200_btae362-B24","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1038\/s42256-021-00325-y","article-title":"Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms","volume":"3","author":"Schulte-Sasse","year":"2021","journal-title":"Nat Mach Intell"},{"key":"2024071814105466200_btae362-B25","doi-asserted-by":"crossref","first-page":"68","DOI":"10.5114\/wo.2014.47136","article-title":"Review the cancer genome atlas (TCGA): an immeasurable source of knowledge","volume":"2015","author":"Tomczak","year":"2015","journal-title":"Contemp Oncol\/Wsp\u00f3\u0142czesna Onkol"},{"key":"2024071814105466200_btae362-B26","author":"Veli\u010dkovi\u0107","year":"2017"},{"key":"2024071814105466200_btae362-B27","doi-asserted-by":"crossref","first-page":"4901","DOI":"10.1093\/bioinformatics\/btac622","article-title":"MODIG: integrating multi-omics and multi-dimensional gene network for cancer driver gene identification based on graph attention network model","volume":"38","author":"Zhao","year":"2022","journal-title":"Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btae362\/58212624\/btae362.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/6\/btae362\/58585485\/btae362.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/6\/btae362\/58585485\/btae362.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T15:35:43Z","timestamp":1721316943000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btae362\/7691993"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":27,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6,3]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btae362","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,6]]},"published":{"date-parts":[[2024,6]]},"article-number":"btae362"}}