A roadmap for multi-omics data integration using deep learning

• Published in: Briefings in bioinformatics Vol. 23; no. 1
• Main Authors: Kang, Mingon, Ko, Euiseong, Mersha, Tesfaye B
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 17.01.2022; Oxford Publishing Limited (England)
• 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; deep learning; imputation; risk prediction; multi-omics; precision medicine; missing value; data integration; harmonization
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbab454

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