{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T01:13:12Z","timestamp":1752282792119,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":36,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819947485"},{"type":"electronic","value":"9789819947492"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-99-4749-2_17","type":"book-chapter","created":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T23:02:17Z","timestamp":1690671737000},"page":"192-206","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MORGAT: A Model Based Knowledge-Informed Multi-omics Integration and Robust Graph Attention Network for Molecular Subtyping of Cancer"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9953-0017","authenticated-orcid":false,"given":"Haobo","family":"Shi","sequence":"first","affiliation":[]},{"given":"Yujie","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Hengyuan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xuan","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7240-5486","authenticated-orcid":false,"given":"Yangkun","family":"Cao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,30]]},"reference":[{"key":"17_CR1","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/1475-2867-6-4","volume":"6","author":"F Grizzi","year":"2006","unstructured":"Grizzi, F., Chiriva-Internati, M.: Cancer: looking for simplicity and finding complexity. Cancer Cell Int. 6, 4 (2006). https:\/\/doi.org\/10.1186\/1475-2867-6-4","journal-title":"Cancer Cell Int."},{"key":"17_CR2","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1007\/s13258-020-01014-7","volume":"42","author":"Y-M Lee","year":"2020","unstructured":"Lee, Y.-M., Oh, M.H., Go, J.-H., Han, K., Choi, S.-Y.: Molecular subtypes of triple-negative breast cancer: understanding of subtype categories and clinical implication. Genes Genom. 42, 1381\u20131387 (2020). https:\/\/doi.org\/10.1007\/s13258-020-01014-7","journal-title":"Genes Genom."},{"issue":"8","key":"17_CR3","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1038\/nrd1157","volume":"2","author":"JK Nicholson","year":"2003","unstructured":"Nicholson, J.K., Wilson, I.D.: Opinion: understanding \u2018global\u2019 systems biology: metabonomics and the continuum of metabolism. Nat. Rev. Drug Discov. 2(8), 668\u2013676 (2003). https:\/\/doi.org\/10.1038\/nrd1157","journal-title":"Nat. Rev. Drug Discov."},{"key":"17_CR4","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/1475-2867-10-11","volume":"10","author":"SS Knox","year":"2010","unstructured":"Knox, S.S.: From \u2018omics\u2019 to complex disease: a systems biology approach to gene-environment interactions in cancer. Cancer Cell Int. 10, 11 (2010). https:\/\/doi.org\/10.1186\/1475-2867-10-11","journal-title":"Cancer Cell Int."},{"issue":"1","key":"17_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bioinformatics\/btv544","volume":"32","author":"Z Yang","year":"2016","unstructured":"Yang, Z., Michailidis, G.: A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data. Bioinformatics 32(1), 1\u20138 (2016). https:\/\/doi.org\/10.1093\/bioinformatics\/btv544","journal-title":"Bioinformatics"},{"issue":"1","key":"17_CR6","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1093\/biostatistics\/kxx017","volume":"19","author":"Q Mo","year":"2018","unstructured":"Mo, Q., Shen, R., Guo, C., Vannucci, M., Chan, K.S., Hilsenbeck, S.G.: A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. Biostatistics 19(1), 71\u201386 (2018). https:\/\/doi.org\/10.1093\/biostatistics\/kxx017","journal-title":"Biostatistics"},{"key":"17_CR7","doi-asserted-by":"publisher","first-page":"477","DOI":"10.3389\/fgene.2018.00477","volume":"9","author":"L Zhang","year":"2018","unstructured":"Zhang, L., et al.: Deep learning-based multi-omics data integration reveals two prognostic subtypes in high-risk neuroblastoma. Front. Genet. 9, 477 (2018). https:\/\/doi.org\/10.3389\/fgene.2018.00477","journal-title":"Front. Genet."},{"issue":"6","key":"17_CR8","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.1158\/1078-0432.CCR-17-0853","volume":"24","author":"K Chaudhary","year":"2018","unstructured":"Chaudhary, K., Poirion, O.B., Lu, L., Garmire, L.X.: Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin. Cancer Res. 24(6), 1248\u20131259 (2018). https:\/\/doi.org\/10.1158\/1078-0432.CCR-17-0853","journal-title":"Clin. Cancer Res."},{"issue":"20","key":"17_CR9","doi-asserted-by":"publisher","first-page":"10546","DOI":"10.1093\/nar\/gky889","volume":"46","author":"N Rappoport","year":"2018","unstructured":"Rappoport, N., Shamir, R.: Multi-omic and multi-view clustering algorithms: review and cancer benchmark. Nucleic Acids Res. 46(20), 10546\u201310562 (2018). https:\/\/doi.org\/10.1093\/nar\/gky889","journal-title":"Nucleic Acids Res."},{"key":"17_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2018","author":"D Sun","year":"2018","unstructured":"Sun, D., Wang, M., Li, A.: A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE\/ACM Trans. Comput. Biol. Bioinform. (2018). https:\/\/doi.org\/10.1109\/TCBB.2018","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"issue":"14","key":"17_CR11","doi-asserted-by":"publisher","first-page":"i501","DOI":"10.1093\/bioinformatics\/btz318","volume":"35","author":"H Sharifi-Noghabi","year":"2019","unstructured":"Sharifi-Noghabi, H., Zolotareva, O., Collins, C.C., Ester, M.: MOLI: multi-omics late integration with deep neural networks for drug response prediction. Bioinformatics 35(14), i501\u2013i509 (2019). https:\/\/doi.org\/10.1093\/bioinformatics\/btz318","journal-title":"Bioinformatics"},{"issue":"1","key":"17_CR12","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1186\/s12859-019-3116-7","volume":"20","author":"J Xu","year":"2019","unstructured":"Xu, J., et al.: A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data. BMC Bioinform. 20(1), 527 (2019). https:\/\/doi.org\/10.1186\/s12859-019-3116-7","journal-title":"BMC Bioinform."},{"issue":"4","key":"17_CR13","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s12975-010-0048-y","volume":"1","author":"M Ning","year":"2010","unstructured":"Ning, M., Lo, E.H.: Opportunities and challenges in omics. Transl. Stroke Res. 1(4), 233\u2013237 (2010). https:\/\/doi.org\/10.1007\/s12975-010-0048-y","journal-title":"Transl. Stroke Res."},{"key":"17_CR14","doi-asserted-by":"publisher","unstructured":"Yang, Z.-Y., Liang, Y., Zhang, H., Chai, H., Zhang, B., Pen, C.: Robust sparse logistic regression with the lq(0 < q < 1) regularization for feature selection using mRNA data. IEEE Access PP, 68586\u201368595 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2880198","DOI":"10.1109\/ACCESS.2018.2880198"},{"key":"17_CR15","doi-asserted-by":"publisher","first-page":"103466","DOI":"10.1016\/j.jbi.2020.103466","volume":"107","author":"Z Momeni","year":"2020","unstructured":"Momeni, Z., et al.: A survey on single and multiomics data mining methods in cancer data classification. J. Biomed. Inform. 107, 103466 (2020). https:\/\/doi.org\/10.1016\/j.jbi.2020.103466","journal-title":"J. Biomed. Inform."},{"issue":"8","key":"17_CR16","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1200\/JCO.2008.18.1370","volume":"27","author":"JS Parker","year":"2009","unstructured":"Parker, J.S., et al.: Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27(8), 1160\u20131167 (2009). https:\/\/doi.org\/10.1200\/JCO.2008.18.1370","journal-title":"J. Clin. Oncol."},{"key":"17_CR17","doi-asserted-by":"publisher","unstructured":"Cancer Genome Atlas Research Network: Comprehensive molecular profiling of lung adenocarcinoma. Nature 511(7511), 543\u2013550 (2014). https:\/\/doi.org\/10.1038\/nature13385","DOI":"10.1038\/nature13385"},{"key":"17_CR18","doi-asserted-by":"publisher","unstructured":"Buhmann, M.D.: Radial Basis Functions: Theory and Implementations. Cambridge Monographs on Applied and Computational Mathematics (2003). https:\/\/doi.org\/10.1017\/CBO9780511543241","DOI":"10.1017\/CBO9780511543241"},{"issue":"18","key":"17_CR19","doi-asserted-by":"publisher","first-page":"3348","DOI":"10.1093\/bioinformatics\/btz058","volume":"35","author":"N Rappoport","year":"2019","unstructured":"Rappoport, N., Shamir, R.: NEMO: cancer subtyping by integration of partial multi-omic data. Bioinformatics 35(18), 3348\u20133356 (2019). https:\/\/doi.org\/10.1093\/bioinformatics\/btz058","journal-title":"Bioinformatics"},{"key":"17_CR20","unstructured":"Chen, Y., et al.: Understanding and improving graph injection attack by promoting unnoticeability. In: International Conference on Learning Representations (ICLR 2022) (2022). arXiv:2202.08057"},{"key":"17_CR21","doi-asserted-by":"publisher","unstructured":"Veli\u010dkovi\u0107, P., et al.: Graph attention networks. In: Proceedings of the International Conference on Learning Representations (ICLR 2018) (2018). https:\/\/doi.org\/10.17863\/CAM.48429","DOI":"10.17863\/CAM.48429"},{"key":"17_CR22","unstructured":"Zhang, X., Zitnik, M.: GNNGuard: defending graph neural networks against adversarial attacks. In: Neural Information Processing Systems (NIPS 2020) (2020). arXiv:2006.08149v3"},{"issue":"5\u20136","key":"17_CR23","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","volume":"18","author":"A Graves","year":"2005","unstructured":"Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5\u20136), 602\u2013610 (2005). https:\/\/doi.org\/10.1016\/j.neunet.2005.06.042","journal-title":"Neural Netw."},{"key":"17_CR24","doi-asserted-by":"publisher","unstructured":"Graves, A., Mohamed, A., Hinton, G.: Speech Recognition with Deep Recurrent Neural Networks (ICASSP 2013) (2013). https:\/\/doi.org\/10.1109\/ICASSP.2013.6638947","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"17_CR25","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: International Conference on Machine Learning (ICML 2017) (2017). arXiv:1703.01365v2"},{"issue":"12","key":"17_CR26","doi-asserted-by":"publisher","first-page":"3047","DOI":"10.3390\/cancers13123047","volume":"13","author":"X Zhang","year":"2021","unstructured":"Zhang, X., Xing, Y., Sun, K., Guo, Y.: OmiEmbed: a unified multi-task deep learning framework for multi-omics data. Cancers (Basel) 13(12), 3047 (2021). https:\/\/doi.org\/10.3390\/cancers13123047","journal-title":"Cancers (Basel)"},{"issue":"1","key":"17_CR27","doi-asserted-by":"publisher","first-page":"3445","DOI":"10.1038\/s41467-021-23774-w","volume":"12","author":"T Wang","year":"2021","unstructured":"Wang, T., et al.: MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification. Nat. Commun. 12(1), 3445 (2021). https:\/\/doi.org\/10.1038\/s41467-021-23774-w","journal-title":"Nat. Commun."},{"issue":"8","key":"17_CR28","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.bbabio.2017.01.004","volume":"1858","author":"S Srinivasan","year":"2017","unstructured":"Srinivasan, S., Guha, M., Kashina, A., Avadhani, N.G.: Mitochondrial dysfunction and mitochondrial dynamics-the cancer connection. Biochim. Biophys. Acta Bioenerg. 1858(8), 602\u2013614 (2017). https:\/\/doi.org\/10.1016\/j.bbabio.2017.01.004","journal-title":"Biochim. Biophys. Acta Bioenerg."},{"issue":"10","key":"17_CR29","doi-asserted-by":"publisher","first-page":"2147","DOI":"10.3390\/cells9102147","volume":"9","author":"B Seitaj","year":"2020","unstructured":"Seitaj, B., et al.: Transmembrane BAX Inhibitor-1 Motif Containing Protein 5 (TMBIM5) sustains mitochondrial structure, shape, and function by impacting the mitochondrial protein synthesis machinery. Cells 9(10), 2147 (2020). https:\/\/doi.org\/10.3390\/cells9102147","journal-title":"Cells"},{"key":"17_CR30","doi-asserted-by":"publisher","unstructured":"Patron, M., et al.: Regulation of mitochondrial proteostasis by the proton gradient. EMBO J. 41(16), e110476 (2022). https:\/\/doi.org\/10.15252\/embj.2021110476","DOI":"10.15252\/embj.2021110476"},{"issue":"1","key":"17_CR31","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.canlet.2013.02.036","volume":"335","author":"P Seshacharyulu","year":"2013","unstructured":"Seshacharyulu, P., Pandey, P., Datta, K., Batra, S.K.: Phosphatase: PP2A structural importance, regulation and its aberrant expression in cancer. Cancer Lett. 335(1), 9\u201318 (2013). https:\/\/doi.org\/10.1016\/j.canlet.2013.02.036","journal-title":"Cancer Lett."},{"issue":"13","key":"17_CR32","doi-asserted-by":"publisher","first-page":"7014","DOI":"10.3390\/ijms23137014","volume":"23","author":"JT Lacerda","year":"2022","unstructured":"Lacerda, J.T., et al.: Lack of TRPV1 channel modulates mouse gene expression and liver proteome with glucose metabolism changes. Int. J. Mol. Sci. 23(13), 7014 (2022). https:\/\/doi.org\/10.3390\/ijms23137014","journal-title":"Int. J. Mol. Sci."},{"issue":"1","key":"17_CR33","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s41021-020-00168-w","volume":"42","author":"A Matsui","year":"2020","unstructured":"Matsui, A., et al.: Oxidation resistance 1 functions in the maintenance of cellular survival and genome stability in response to oxidative stress-independent DNA damage. Genes Environ. 42(1), 29 (2020). https:\/\/doi.org\/10.1186\/s41021-020-00168-w","journal-title":"Genes Environ."},{"issue":"2","key":"17_CR34","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.ccell.2020.06.001","volume":"38","author":"JD Hayes","year":"2020","unstructured":"Hayes, J.D., Dinkova-Kostova, A.T., Tew, K.D.: Oxidative stress in cancer. Cancer Cell 38(2), 167\u2013197 (2020). https:\/\/doi.org\/10.1016\/j.ccell.2020.06.001","journal-title":"Cancer Cell"},{"issue":"4","key":"17_CR35","doi-asserted-by":"publisher","first-page":"506","DOI":"10.4161\/auto.6.4.11863","volume":"6","author":"HE Polson","year":"2010","unstructured":"Polson, H.E., et al.: Mammalian Atg18 (WIPI2) localizes to omegasome-anchored phagophores and positively regulates LC3 lipidation. Autophagy 6(4), 506\u2013522 (2010). https:\/\/doi.org\/10.4161\/auto.6.4.11863","journal-title":"Autophagy"},{"key":"17_CR36","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1101\/sqb.2016.81.030981","volume":"81","author":"JY Guo","year":"2016","unstructured":"Guo, J.Y., White, E.: Autophagy, metabolism, and cancer. Cold Spring Harb. Symp. Quant. Biol. 81, 73\u201378 (2016). https:\/\/doi.org\/10.1101\/sqb.2016.81.030981","journal-title":"Cold Spring Harb. Symp. Quant. Biol."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-4749-2_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T06:04:27Z","timestamp":1693548267000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-4749-2_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819947485","9789819947492"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-4749-2_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2023a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2023\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}