A roadmap for multi-omics data integration using deep learning

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.

Automated summary

High-throughput next-generation sequencing allows for the generation of large amounts of multi-omics data, which has revolutionized biomedical research by providing a more comprehensive understanding of biological systems and disease development mechanisms. Deep learning algorithms have emerged as a promising method in multi-omics data analysis due to their predictive performance and ability to capture nonlinear and hierarchical features. However, integrating and translating multi-omics data into functional insights remains a challenge, but there is a clear trend towards incorporating multi-omics analysis in biomedical research to explain complex relationships between molecular layers.