Haimei Wang

Hainan Institute of Biotechnology, Haikou, 570206, Hainan, China
Author    Correspondence author
Computational Molecular Biology, 2024, Vol. 14, No. 2   
Received: 17 Feb., 2024    Accepted: 29 Mar., 2024    Published: 16 Apr., 2024

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.

As biological data explosively grows and traditional computational methods struggle to keep pace, deep learning has become a powerful tool for analyzing complex biological data, significantly improving the ability to mine and interpret large-scale biological data, including images, signals, and sequences. This study reviews successful applications of deep learning in key areas such as genomics, proteomics, and drug discovery, and the results show that deep learning models outperform traditional methods in tasks such as gene expression prediction and protein structure modeling. Deep learning offers great potential for advancing bioinformatics research to analyze biological data more accurately and efficiently, but many challenges remain, and future research should focus on addressing identified challenges and exploring new applications of deep learning in bioinformatics to fully realize its potential.

Deep learning; Bioinformatics; Neural networks; Data mining; Biomedical data