A roadmap for multi-omics data integration using deep learning
Open accessAbstract
High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. 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.
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Deep learning has been utilized for integrating multi-omics datasets and performing different downstream tasks.
“Deep learning has been utilized for integrating multi-omics datasets and performing different downstream tasks [111].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [111]
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- Authors
- Kang, M., Ko, E., & Mersha, T. B.
- Affiliations
- University of Nevada, Las Vegas (US)
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-11-12
- Volume
- 23
- Issue
- 1
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- PMC
- PMC8769688
- OpenAlex
- W3213224786
- Cited by
- 389
- References
- 8
- Citations by year
- 2026: 79 · 2025: 121 · 2024: 106 · 2023: 58 · 2022: 25
- FWCI
- 5.04
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Briefings in Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Heart, Lung, and Blood Institute (R01 HL132344)
- Institute for Information and Communications Technology Planning and Evaluation (2019-0-01601)
- Ministry of Science and ICT
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- multi-omics integration, deep learning, next-generation sequencing, disease mechanisms, endotyping, personalized treatment, implementation barriers
- MeSH terms
- Deep Learning, Algorithms, Genomics, High-Throughput Nucleotide Sequencing
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