A roadmap for multi-omics data integration using deep learning

Abstract 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 d...

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Bibliographic Details
Published in:Briefings in bioinformatics 2022-01, Vol.23 (1)
Main Authors: Kang, Mingon, Ko, Euiseong, Mersha, Tesfaye B
Format: Article
Language:English
Subjects:
Algorithms
Biomedical data
Biomedical research
Data analysis
Data integration
Deep Learning
Genomics
High-Throughput Nucleotide Sequencing
Integration
Machine learning
Medical research
Molecular modelling
Next-generation sequencing
Performance prediction
Review
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Summary:Abstract 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. Graphical Abstract Graphical Abstract
ISSN:1467-5463
1477-4054
DOI:10.1093/bib/bbab454