{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T23:03:44Z","timestamp":1784070224163,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":37,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819237159","type":"print"},{"value":"9789819237166","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-981-92-3716-6_9","type":"book-chapter","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T22:07:36Z","timestamp":1784066856000},"page":"109-124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BioMOE-CDG: Pretrained Biological Sequence Embedding-Guided MoE for Cancer Driver Gene Prediction"],"prefix":"10.1007","author":[{"given":"Yang","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,15]]},"reference":[{"issue":"1","key":"9_CR1","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.cell.2013.03.002","volume":"153","author":"LA Garraway","year":"2013","unstructured":"Garraway, L.A., Lander, E.S.: Lessons from the cancer genome. Cell 153(1), 17\u201337 (2013)","journal-title":"Cell"},{"issue":"7484","key":"9_CR2","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1038\/nature12912","volume":"505","author":"MS Lawrence","year":"2014","unstructured":"Lawrence, M.S., Stojanov, P., Mermel, C.H., et al.: Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505(7484), 495\u2013501 (2014)","journal-title":"Nature"},{"issue":"10","key":"9_CR3","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1038\/ng.2764","volume":"45","author":"JN Weinstein","year":"2013","unstructured":"Weinstein, J.N., Collisson, E.A., Mills, G.B., et al.: The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45(10), 1113\u20131120 (2013)","journal-title":"Nat. Genet."},{"issue":"4","key":"9_CR4","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1038\/s41587-019-0055-9","volume":"37","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Bajari, R., Andric, D., et al.: The international cancer genome consortium data portal. Nat. Biotechnol. 37(4), 367\u2013369 (2019)","journal-title":"Nat. Biotechnol."},{"issue":"D1","key":"9_CR5","doi-asserted-by":"publisher","first-page":"D941","DOI":"10.1093\/nar\/gky1015","volume":"47","author":"JG Tate","year":"2019","unstructured":"Tate, J.G., Bamford, S., Jubb, H.C., et al.: COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 47(D1), D941\u2013D947 (2019)","journal-title":"Nucleic Acids Res."},{"issue":"7457","key":"9_CR6","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1038\/nature12213","volume":"499","author":"MS Lawrence","year":"2013","unstructured":"Lawrence, M.S., Stojanov, P., Polak, P., et al.: Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499(7457), 214\u2013218 (2013)","journal-title":"Nature"},{"issue":"18","key":"9_CR7","doi-asserted-by":"publisher","first-page":"2238","DOI":"10.1093\/bioinformatics\/btt395","volume":"29","author":"D Tamborero","year":"2013","unstructured":"Tamborero, D., Gonzalez-Perez, A., Lopez-Bigas, N.: OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes. Bioinformatics 29(18), 2238\u20132244 (2013)","journal-title":"Bioinformatics"},{"issue":"4","key":"9_CR8","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1038\/nmeth0410-248","volume":"7","author":"IA Adzhubei","year":"2010","unstructured":"Adzhubei, I.A., Schmidt, S., Peshkin, L., et al.: A method and server for predicting damaging missense mutations. Nat. Methods 7(4), 248\u2013249 (2010)","journal-title":"Nat. Methods"},{"issue":"13","key":"9_CR9","doi-asserted-by":"publisher","first-page":"3812","DOI":"10.1093\/nar\/gkg509","volume":"31","author":"PC Ng","year":"2003","unstructured":"Ng, P.C., Henikoff, S.: SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31(13), 3812\u20133814 (2003)","journal-title":"Nucleic Acids Res."},{"issue":"4","key":"9_CR10","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1093\/bioinformatics\/btw662","volume":"33","author":"H Yang","year":"2017","unstructured":"Yang, H., Wei, Q., Zhong, X., et al.: Cancer driver gene discovery through an integrative genomics approach in a non-parametric Bayesian framework. Bioinformatics 33(4), 483\u2013490 (2017)","journal-title":"Bioinformatics"},{"issue":"1","key":"9_CR11","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1038\/s41467-019-13803-0","volume":"11","author":"A Colaprico","year":"2020","unstructured":"Colaprico, A., Olsen, C., Bailey, M.H., et al.: Interpreting pathways to discover cancer driver genes with moonlight. Nat. Commun. 11(1), 69 (2020)","journal-title":"Nat. Commun."},{"issue":"12","key":"9_CR12","doi-asserted-by":"publisher","first-page":"R124","DOI":"10.1186\/gb-2012-13-12-r124","volume":"13","author":"A Bashashati","year":"2012","unstructured":"Bashashati, A., Haffari, G., Ding, J., et al.: DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer. Genome Biol. 13(12), R124 (2012)","journal-title":"Genome Biol."},{"issue":"1","key":"9_CR13","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1186\/s13059-016-0989-x","volume":"17","author":"A Cho","year":"2016","unstructured":"Cho, A., Shim, J.E., Kim, E., et al.: MUFFINN: cancer gene discovery via network analysis of somatic mutation data. Genome Biol. 17(1), 129 (2016)","journal-title":"Genome Biol."},{"issue":"2","key":"9_CR14","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1038\/ng.3168","volume":"47","author":"MDM Leiserson","year":"2015","unstructured":"Leiserson, M.D.M., Vandin, F., Wu, H.T., et al.: Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet. 47(2), 106\u2013114 (2015)","journal-title":"Nat. Genet."},{"issue":"Suppl. 17","key":"9_CR15","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1186\/s12859-016-1332-y","volume":"17","author":"PJ Wei","year":"2016","unstructured":"Wei, P.J., Zhang, D., Xia, J., et al.: LNDriver: identifying driver genes by integrating mutation and expression data based on gene-gene interaction network. BMC Bioinform. 17(Suppl. 17), 467 (2016)","journal-title":"BMC Bioinform."},{"issue":"1","key":"9_CR16","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1186\/s12859-019-2847-9","volume":"20","author":"J Song","year":"2019","unstructured":"Song, J., Peng, W., Wang, F.: A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph. BMC Bioinform. 20(1), 238 (2019)","journal-title":"BMC Bioinform."},{"issue":"3","key":"9_CR17","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1109\/TCBB.2019.2897931","volume":"17","author":"J Song","year":"2019","unstructured":"Song, J., Peng, W., Wang, F.: An entropy-based method for identifying mutual exclusive driver genes in cancer. IEEE\/ACM Trans. Comput. Biol. Bioinf. 17(3), 758\u2013768 (2019)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"issue":"6","key":"9_CR18","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1038\/s42256-021-00325-y","volume":"3","author":"R Schulte-Sasse","year":"2021","unstructured":"Schulte-Sasse, R., Budach, S., Hnisz, D., et al.: Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms. Nat. Mach. Intell. 3(6), 513\u2013526 (2021)","journal-title":"Nat. Mach. Intell."},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Peng, W., Tang, Q., Dai, W., et al.: Improving cancer driver gene identification using multi-task learning on graph convolutional network. Briefings Bioinform. 23(1), bbab432 (2022)","DOI":"10.1093\/bib\/bbab432"},{"issue":"8","key":"9_CR20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1012400","volume":"20","author":"T Wang","year":"2024","unstructured":"Wang, T., Zhuo, L., Chen, Y., et al.: ECD-CDGI: an efficient energy-constrained diffusion model for cancer driver gene identification. PLoS Comput. Biol. 20(8), e1012400 (2024)","journal-title":"PLoS Comput. Biol."},{"issue":"6","key":"9_CR21","doi-asserted-by":"publisher","first-page":"1650","DOI":"10.1007\/s11390-025-4999-6","volume":"40","author":"W Peng","year":"2025","unstructured":"Peng, W., Xu, X.P., Dai, W., et al.: Predicting cancer driver genes via contrastive graph diffusion and dynamic weighting. J. Comput. Sci. Technol. 40(6), 1650\u20131661 (2025)","journal-title":"J. Comput. Sci. Technol."},{"issue":"4","key":"9_CR22","doi-asserted-by":"publisher","first-page":"3430","DOI":"10.1109\/TNSE.2024.3373652","volume":"11","author":"W Peng","year":"2024","unstructured":"Peng, W., Zhou, Z., Dai, W., et al.: Multi-network graph contrastive learning for cancer driver gene identification. IEEE Trans. Netw. Sci. Eng. 11(4), 3430\u20133440 (2024)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"21","key":"9_CR23","doi-asserted-by":"publisher","first-page":"4901","DOI":"10.1093\/bioinformatics\/btac622","volume":"38","author":"W Zhao","year":"2022","unstructured":"Zhao, W., Gu, X., Chen, S., et al.: MODIG: integrating multi-omics and multi-dimensional gene network for cancer driver gene identification based on graph attention network model. Bioinformatics 38(21), 4901\u20134907 (2022)","journal-title":"Bioinformatics"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, D., Zhang, W., Zhao, Y., et al.: DNAGPT: a generalized pre-trained tool for versatile DNA sequence analysis tasks. arXiv preprint arXiv:2307.05628 (2023)","DOI":"10.1101\/2023.07.11.548628"},{"key":"9_CR25","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"9_CR26","unstructured":"Shazeer, N., Mirhoseini, A., Maziarz, K., et al.: Outrageously large neural networks: the sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538 (2017)"},{"issue":"D1","key":"9_CR27","doi-asserted-by":"publisher","first-page":"D1053","DOI":"10.1093\/nar\/gkaf1239","volume":"54","author":"AD Yates","year":"2026","unstructured":"Yates, A.D., Austine-Orimoloye, O., Azov, A.G., et al.: Ensembl 2026. Nucleic Acids Res. 54(D1), D1053\u2013D1060 (2026)","journal-title":"Nucleic Acids Res."},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Wang, X., Yang, C.: MoE-health: a mixture of experts framework for robust multimodal healthcare prediction. In: Proceedings of the 16th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 1\u20139 (2025)","DOI":"10.1145\/3765612.3767207"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Li, Y., Jiang, S., Hu, B., et al.: Uni-MoE: scaling unified multimodal LLMs with mixture of experts. IEEE Trans. Pattern Anal. Mach. Intell. (2025)","DOI":"10.1109\/TPAMI.2025.3532688"},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Du, Z., An, J., Tu, Y., et al.: Mixture-of-experts for open set domain adaptation: a dual-space detection approach. IEEE Trans. Artif. Intell. (2025)","DOI":"10.1109\/TAI.2025.3560590"},{"key":"9_CR31","doi-asserted-by":"crossref","unstructured":"Kamburov, A., Pentchev, K., Galicka, H., et al.: ConsensusPathDB: toward a more complete picture of cell biology. Nucleic Acids Res. 39(Suppl_1), D712\u2013D717 (2011)","DOI":"10.1093\/nar\/gkq1156"},{"issue":"D1","key":"9_CR32","doi-asserted-by":"publisher","first-page":"D607","DOI":"10.1093\/nar\/gky1131","volume":"47","author":"D Szklarczyk","year":"2019","unstructured":"Szklarczyk, D., Gable, A.L., Lyon, D., et al.: STRING v11: protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47(D1), D607\u2013D613 (2019)","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"9_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13059-018-1612-0","volume":"20","author":"D Repana","year":"2019","unstructured":"Repana, D., Nulsen, J., Dressler, L., et al.: The Network of Cancer Genes (NCG): a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens. Genome Biol. 20(1), 1 (2019)","journal-title":"Genome Biol."},{"key":"9_CR34","doi-asserted-by":"crossref","unstructured":"Hamosh, A., Scott, A.F., Amberger, J.S., et al.: Online mendelian inheritance in man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33(Suppl_1), D514\u2013D517 (2005)","DOI":"10.1093\/nar\/gki033"},{"issue":"1","key":"9_CR35","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1093\/nar\/28.1.27","volume":"28","author":"M Kanehisa","year":"2000","unstructured":"Kanehisa, M., Goto, S.: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28(1), 27\u201330 (2000)","journal-title":"Nucleic Acids Res."},{"key":"9_CR36","doi-asserted-by":"crossref","unstructured":"Deng, C., Li, H.D., Zhang, L.S., et al.: Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks. Bioinformatics 40(Suppl._1), i511\u2013i520 (2024)","DOI":"10.1093\/bioinformatics\/btae257"},{"key":"9_CR37","doi-asserted-by":"crossref","unstructured":"Deng, C., Li, H., Wang, J.: Improving cancer gene prediction by enhancing common information between the PPI network and gene functional association. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, pp. 137\u2013145 (2025)","DOI":"10.1609\/aaai.v39i1.31989"}],"container-title":["Lecture Notes in Computer Science","Bioinformatics Research and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3716-6_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T22:07:38Z","timestamp":1784066858000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3716-6_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,15]]},"ISBN":["9789819237159","9789819237166"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3716-6_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,15]]},"assertion":[{"value":"15 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISBRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Bioinformatics Research and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isbra2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}