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These data have revolutionized biomedical research by providing a more comprehensive understanding of the biological systems and molecular mechanisms of disease development. Recently, deep learning (DL) algorithms have become one of the most promising methods in multi-omics data analysis, due to their predictive performance and capability of capturing nonlinear and hierarchical features. While integrating and translating multi-omics data into useful functional insights remain the biggest bottleneck, there is a clear trend towards incorporating multi-omics analysis in biomedical research to help explain the complex relationships between molecular layers. Multi-omics data have a role to improve prevention, early detection and prediction; monitor progression; interpret patterns and endotyping; and design personalized treatments. In this review, we outline a roadmap of multi-omics integration using DL and offer a practical perspective into the advantages, challenges and barriers to the implementation of DL in multi-omics data.<\/jats:p>","DOI":"10.1093\/bib\/bbab454","type":"journal-article","created":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T09:08:05Z","timestamp":1633597685000},"source":"Crossref","is-referenced-by-count":363,"title":["A roadmap for multi-omics data integration using deep learning"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9565-9523","authenticated-orcid":false,"given":"Mingon","family":"Kang","sequence":"first","affiliation":[{"name":"Department of Computer Science at the University of Nevada, Las Vegas, NV, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8522-5334","authenticated-orcid":false,"given":"Euiseong","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of 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