Hongwei Liu

Modern Agricultural Research Center of Cuixi Academy of Biotechology, Zhuji, 311800, Zhejiang, China
Author    Correspondence author
Computational Molecular Biology, 2024, Vol. 14, No. 5   
Received: 23 Aug., 2024    Accepted: 25 Sep., 2024    Published: 30 Oct., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This study explores integrative approaches in computational genomics that combine multi-omics data to study gene evolution. It provides a detailed analysis of the key components of genomics, transcriptomics, proteomics, and epigenomics, clarifying their roles in gene evolution research. Furthermore, it discusses computational techniques such as network-based methods, machine learning, and gene set analysis, which enhance the integration and interpretation of multi-omics data. With the advancement of high-throughput technologies, multi-omics integration has become a vital approach to understanding the complexity of gene evolution, as it offers a comprehensive perspective on how changes in the genome, transcriptome, proteome, and epigenome drive evolutionary processes. Through case studies in agriculture, medicine, and microbial evolution, this study emphasizes the practical applications of multi-omics integration, reveals the molecular mechanisms behind gene evolution, and provides guidance for future research and applications across various fields.

Multi-omics integration; Gene evolution; Computational genomics; Machine learning; Epigenomics