Shiying Yu

Biotechnology Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, China
Author    Correspondence author
Computational Molecular Biology, 2025, Vol. 15, No. 3   doi: 10.5376/cmb.2025.15.0011
Received: 03 Mar., 2025    Accepted: 14 Apr., 2025    Published: 02 May, 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.

Yu S.Y., 2025, Deep learning for predicting gene expression from genomic sequences, Computational Molecular Biology, 15(3): 112-121 (doi: 10.5376/cmb.2025.15.0011)

 

Different cell types of higher organisms share the same genomic sequence but have distinct gene expressions, which is attributed to complex gene regulatory mechanisms. Cracking the regulatory rules of gene expression is of vital importance for understanding diseases and life processes. This review examines the research progress on predicting gene expression from genomic sequences using deep learning, including data sources and processing, model architecture design, prediction methods, performance evaluation and interpretability analysis, current challenges and the latest advancements, and illustrates them through case studies of specific species. Finally, the prospects of the integration of deep learning and multi-omics in the future and its potential impact in precision medicine and functional genomics were prospected.

Deep learning; Genomic sequence; Gene expression; Gene regulation; Multiomics integration