Artificial Intelligence for Central Dogma-Centric Multi-Omics: Challenges and Breakthroughs
Journal:
arXiv
Published Date:
Dec 17, 2024
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.
