{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T16:14:58Z","timestamp":1776356098822,"version":"3.51.2"},"reference-count":51,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-18-IBHU-0002"],"award-info":[{"award-number":["ANR-18-IBHU-0002"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE\/ACM Trans. Comput. Biol. and Bioinf."],"published-print":{"date-parts":[[2022,1,1]]},"DOI":"10.1109\/tcbb.2021.3060340","type":"journal-article","created":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T21:08:00Z","timestamp":1613682480000},"page":"135-145","source":"Crossref","is-referenced-by-count":13,"title":["Representation Learning for the Clustering of Multi-Omics Data"],"prefix":"10.1109","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4731-6155","authenticated-orcid":false,"given":"Gautier","family":"Viaud","sequence":"first","affiliation":[]},{"given":"Prasanna","family":"Mayilvahanan","sequence":"additional","affiliation":[]},{"given":"Paul-Henry","family":"Cournede","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.molonc.2010.11.003"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/nature10983"},{"issue":"7418","key":"ref3","first-page":"61","article-title":"Comprehensive molecular portraits of human breast tumours","volume-title":"Nature","volume":"490","year":"2012"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbw113"},{"key":"ref5","article-title":"More is better: Recent progress in multi-omics data integration methods","volume-title":"Frontiers Genetics","volume":"8","author":"Huang","year":"2017"},{"issue":"13","key":"ref6","first-page":"i401","article-title":"A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules","volume-title":"Bioinf.","volume":"27","author":"Zhang","year":"2011"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btp543"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1208949110"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bts595"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btt425"},{"issue":"3","key":"ref11","first-page":"333","article-title":"Similarity network fusion for aggregating data types on a genomic scale","volume-title":"Nat. Methods","volume":"11","author":"Wang","year":"2014"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2018.09.012"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.612"},{"key":"ref14","first-page":"478","article-title":"Unsupervised deep embedding for clustering analysis","volume-title":"Proc. 33rd Int. Conf. Mach. Learn.","author":"Xie"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/243"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_9"},{"key":"ref17","article-title":"Boosting gene expression clustering with system-wide biological information: A robust autoencoder approach","volume-title":"bioRxiv","volume":"1","author":"Cui","year":"2017"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098052"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-017-4226-0"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-019-3298-z"},{"key":"ref21","article-title":"Deep learning-based multi-omics data integration reveals two prognostic subtypes in high-risk neuroblastoma","volume-title":"Front. Genet.","volume":"9","author":"Zhang","year":"2018"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1158\/1078-0432.CCR-17-0853"},{"issue":"6","key":"ref23","article-title":"Evaluation of colorectal cancer subtypes and cell lines using deep learning","volume-title":"Life Sci. Alliance","volume":"2","author":"Ronen","year":"2019"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90014-2"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"ref26","article-title":"From principal subspaces to principal components with linear autoencoders","author":"Plaut","year":"2018"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1039\/C7MO00051K"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.coisb.2019.03.007"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1005752"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.15252\/msb.20178124"},{"key":"ref31","volume-title":"Analyses factorielles simples et multiples. Objectifs m\u00e9thodes et interpr\u00e9tation","author":"Escofier","year":"2008"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1186\/s13062-018-0207-8"},{"issue":"2","key":"ref33","first-page":"269","article-title":"Precision oncology beyond targeted therapy: Combining omics data with machine learning matches the majority of cancer cells to effective therapeutics","volume-title":"Mol. Cancer Res.","volume":"16","author":"Ding","year":"2018"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM.2018.8621379"},{"issue":"14","key":"ref35","first-page":"i501","article-title":"MOLI: Multi-omics late integration with deep neural networks for drug response prediction","volume-title":"Bioinformatics","volume":"35","author":"Sharifi-Noghabi","year":"2019"},{"key":"ref36","first-page":"3546","article-title":"Semi-supervised learning with ladder networks","volume-title":"Proc. 28th Int. Conf. Neural Inf. Process. Syst.","author":"Rasmus"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/bf02294245"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CIMCA.2005.1631265"},{"issue":"1","key":"ref39","first-page":"243","article-title":"An extensive comparative study of cluster validity indices","volume-title":"Pattern Recognit.","volume":"46","author":"Arbelaitz","year":"2013"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/GSCIT.2015.7353330"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btq182"},{"key":"ref42","article-title":"Auto-encoding variational bayes","author":"Kingma","year":"2013"},{"key":"ref43","article-title":"beta-VAE: Learning basic visual concepts with a constrained variational framework","volume-title":"Proc. 5th Int. Conf. Learn. Representations","author":"Higgins"},{"key":"ref44","article-title":"Recent advances in autoencoder-based representation learning","author":"Tschannen","year":"2018"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207046"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11867"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2019.01205"},{"key":"ref48","first-page":"1061","article-title":"Autoencoding any data through kernel autoencoders","volume-title":"Proc. Mach. Learn. Res.","author":"Laforgue"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017467"},{"key":"ref50","first-page":"119","article-title":"Oi-VAE: Output interpretable VAEs for nonlinear group factor analysis","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ainsworth"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1038\/s41596-018-0103-9"}],"container-title":["IEEE\/ACM Transactions on Computational Biology and Bioinformatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8857\/9702518\/09357965.pdf?arnumber=9357965","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T23:49:02Z","timestamp":1704844142000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9357965\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,1]]},"references-count":51,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tcbb.2021.3060340","relation":{},"ISSN":["1545-5963","1557-9964","2374-0043"],"issn-type":[{"value":"1545-5963","type":"print"},{"value":"1557-9964","type":"electronic"},{"value":"2374-0043","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,1]]}}}