Artificial Intelligence for Central Dogma-Centric Multi-Omics: Challenges and Breakthroughs

TL;DR

This paper reviews how artificial intelligence and deep learning are advancing multi-omics data integration and analysis, addressing challenges like high dimensionality and noise to improve disease prediction and understanding of the central dogma.

Contribution

It provides a comprehensive overview of AI-driven multi-omics models, strategies for data integration, and recent breakthroughs, offering guidance for computational biologists.

Findings

AI models improve disease prediction accuracy

Integration of multi-omics data reveals genetic loci

Breakthroughs in multi-omics technologies

Abstract

With the rapid development of high-throughput sequencing platforms, an increasing number of omics technologies, such as genomics, metabolomics, and transcriptomics, are being applied to disease genetics research. However, biological data often exhibit high dimensionality and significant noise, making it challenging to effectively distinguish disease subtypes using a single-omics approach. To address these challenges and better capture the interactions among DNA, RNA, and proteins described by the central dogma, numerous studies have leveraged artificial intelligence to develop multi-omics models for disease research. These AI-driven models have improved the accuracy of disease prediction and facilitated the identification of genetic loci associated with diseases, thus advancing precision medicine. This paper reviews the mathematical definitions of multi-omics, strategies for integrating multi-omics data, applications of artificial intelligence and deep learning in multi-omics, the establishment of foundational models, and breakthroughs in multi-omics technologies, drawing insights from over 130 related articles. It aims to provide practical guidance for computational biologists to better understand and effectively utilize AI-based multi-omics machine learning algorithms in the context of central dogma.