{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T18:35:40Z","timestamp":1776882940782,"version":"3.51.2"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031258909","type":"print"},{"value":"9783031258916","type":"electronic"}],"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\/978-3-031-25891-6_5","type":"book-chapter","created":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T14:03:34Z","timestamp":1678370614000},"page":"47-61","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Multi-omic Data Integration and\u00a0Feature Selection for\u00a0Survival-Based Patient Stratification via\u00a0Supervised Concrete Autoencoders"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0347-7002","authenticated-orcid":false,"given":"Pedro Henrique","family":"da Costa Avelar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0118-4548","authenticated-orcid":false,"given":"Roman","family":"Laddach","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4100-7810","authenticated-orcid":false,"given":"Sophia N.","family":"Karagiannis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0977-3600","authenticated-orcid":false,"given":"Min","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8403-1282","authenticated-orcid":false,"given":"Sophia","family":"Tsoka","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"issue":"4","key":"5_CR1","doi-asserted-by":"publisher","first-page":"524","DOI":"10.3390\/biom10040524","volume":"10","author":"K Asada","year":"2020","unstructured":"Asada, K., et al.: Uncovering prognosis-related genes and pathways by multi-omics analysis in lung cancer. Biomolecules 10(4), 524 (2020)","journal-title":"Biomolecules"},{"key":"5_CR2","unstructured":"Bal\u0131n, M.F., Abid, A., Zou, J.: Concrete autoencoders: differentiable feature selection and reconstruction. In: International Conference on Machine Learning, pp. 444\u2013453. PMLR (2019)"},{"key":"5_CR3","doi-asserted-by":"publisher","unstructured":"Bingham, E., Mannila, H.: Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of the 7th ACM SIGKDD, KDD 2001, pp. 245\u2013250. Association for Computing Machinery, New York (2001). https:\/\/doi.org\/10.1145\/502512.502546","DOI":"10.1145\/502512.502546"},{"issue":"1","key":"5_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41698-017-0010-5","volume":"1","author":"AM Bode","year":"2017","unstructured":"Bode, A.M., Dong, Z.: Precision oncology-the future of personalized cancer medicine? NPJ Precis. Oncol. 1(1), 1\u20132 (2017). https:\/\/doi.org\/10.1038\/s41698-017-0010-5","journal-title":"NPJ Precis. Oncol."},{"issue":"1","key":"5_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-20430-7","volume":"12","author":"L Cantini","year":"2021","unstructured":"Cantini, L., et al.: Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nat. Commun. 12(1), 1\u201312 (2021)","journal-title":"Nat. Commun."},{"issue":"6","key":"5_CR6","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. Can. Res. 24(6), 1248\u20131259 (2018)","journal-title":"Clin. Can. Res."},{"issue":"4","key":"5_CR7","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1006076","volume":"14","author":"T Ching","year":"2018","unstructured":"Ching, T., Zhu, X., Garmire, L.X.: Cox-nnet: an artificial neural network method for prognosis prediction of high-throughput omics data. PLoS Comput. Biol. 14(4), e1006076 (2018)","journal-title":"PLoS Comput. Biol."},{"key":"5_CR8","doi-asserted-by":"publisher","first-page":"166","DOI":"10.3389\/fgene.2019.00166","volume":"10","author":"Z Huang","year":"2019","unstructured":"Huang, Z., et al.: SALMON: survival analysis learning with multi-omics neural networks on breast cancer. Front. Genet. 10, 166 (2019)","journal-title":"Front. Genet."},{"issue":"1","key":"5_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12874-018-0482-1","volume":"18","author":"JL Katzman","year":"2018","unstructured":"Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., Kluger, Y.: DeepSurv: personalized treatment recommender system using a cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18(1), 1\u201312 (2018)","journal-title":"BMC Med. Res. Methodol."},{"key":"5_CR10","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: Bengio, Y., LeCun, Y. (eds.) 2nd ICLR, Banff, AB, Canada, 14\u201316 April 2014, Conference Track Proceedings (2014)"},{"issue":"2","key":"5_CR11","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1165\/rcmb.2017-0430TR","volume":"59","author":"CM Koch","year":"2018","unstructured":"Koch, C.M., et al.: A beginner\u2019s guide to analysis of RNA sequencing data. Am. J. Respir. Cell Mol. Biol. 59(2), 145\u2013157 (2018)","journal-title":"Am. J. Respir. Cell Mol. Biol."},{"issue":"12","key":"5_CR12","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1038\/s41592-019-0619-0","volume":"16","author":"I Korsunsky","year":"2019","unstructured":"Korsunsky, I., et al.: Fast, sensitive and accurate integration of single-cell data with harmony. Nat. Methods 16(12), 1289\u20131296 (2019)","journal-title":"Nat. Methods"},{"issue":"5795","key":"5_CR13","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1126\/science.1132939","volume":"313","author":"J Lamb","year":"2006","unstructured":"Lamb, J., et al.: The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313(5795), 1929\u20131935 (2006). https:\/\/doi.org\/10.1126\/science.1132939","journal-title":"Science"},{"key":"5_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2020.107277","volume":"87","author":"TY Lee","year":"2020","unstructured":"Lee, T.Y., Huang, K.Y., Chuang, C.H., Lee, C.Y., Chang, T.H.: Incorporating deep learning and multi-omics autoencoding for analysis of lung adenocarcinoma prognostication. Comput. Biol. Chem. 87, 107277 (2020)","journal-title":"Comput. Biol. Chem."},{"key":"5_CR15","unstructured":"Maddison, C.J., Mnih, A., Teh, Y.W.: The concrete distribution: a continuous relaxation of discrete random variables. arXiv:1611.00712 [cs, stat] (2017)"},{"key":"5_CR16","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.3389\/fonc.2020.01030","volume":"10","author":"G Nicora","year":"2020","unstructured":"Nicora, G., Vitali, F., Dagliati, A., Geifman, N., Bellazzi, R.: Integrated multi-omics analyses in oncology: a review of machine learning methods and tools. Front. Oncol. 10, 1030 (2020)","journal-title":"Front. Oncol."},{"issue":"1","key":"5_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13073-021-00930-x","volume":"13","author":"OB Poirion","year":"2021","unstructured":"Poirion, O.B., Jing, Z., Chaudhary, K., Huang, S., Garmire, L.X.: DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data. Genome Med. 13(1), 1\u201315 (2021)","journal-title":"Genome Med."},{"key":"5_CR18","doi-asserted-by":"publisher","unstructured":"King\u2019s College London e Research Team: King\u2019s Computational Research, Engineering and Technology Environment (CREATE) (2022). https:\/\/doi.org\/10.18742\/RNVF-M076. https:\/\/docs.er.kcl.ac.uk\/","DOI":"10.18742\/RNVF-M076"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Ronen, J., Hayat, S., Akalin, A.: Evaluation of colorectal cancer subtypes and cell lines using deep learning. Life Sci. Alliance 2(6) (2019)","DOI":"10.26508\/lsa.201900517"},{"issue":"1","key":"5_CR20","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1186\/s12911-020-01225-8","volume":"20","author":"L Tong","year":"2020","unstructured":"Tong, L., Mitchel, J., Chatlin, K., Wang, M.D.: Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis. BMC Med. Inform. Decis. Mak. 20(1), 225 (2020). https:\/\/doi.org\/10.1186\/s12911-020-01225-8","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Uyar, B., Ronen, J., Franke, V., Gargiulo, G., Akalin, A.: Multi-omics and deep learning provide a multifaceted view of cancer. bioRxiv (2021)","DOI":"10.1101\/2021.09.29.462364"},{"key":"5_CR22","doi-asserted-by":"publisher","unstructured":"Wissel, D., Rowson, D., Boeva, V.: Hierarchical autoencoder-based integration improves performance in multi-omics cancer survival models through soft modality selection. Technical report, bioRxiv (2022). https:\/\/doi.org\/10.1101\/2021.09.16.460589. Section: New Results Type: article","DOI":"10.1101\/2021.09.16.460589"},{"key":"5_CR23","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)","journal-title":"Front. Genet."}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25891-6_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T10:14:20Z","timestamp":1680689660000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25891-6_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031258909","9783031258916"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25891-6_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"10 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Certosa di Pontignano","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"lod2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2022.icas.cc\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"226","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5.6","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}