Xiaoming Liu , Wei Zhang

WuXi AppTec Co., Ltd, Wuxi, 518083, Jiangsu, China
Author    Correspondence author
Computational Molecular Biology, 2024, Vol. 14, No. 4   
Received: 20 Jun., 2024    Accepted: 05 Aug., 2024    Published: 25 Aug., 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.

The rise of big data has changed the landscape of bioinformatics, providing new opportunities for biological discoveries, but also bringing significant computational challenges. This study provides an in-depth analysis of bioinformatics in the era of big data, focusing on the evolution of computing tools and their role in modern biology. It reviews the usage process from early bioinformatics tools to current high-throughput data analysis, as well as the expansion of public biological databases. In the context of genomics, proteomics, and multi omics integration, key computing methods, including machine learning algorithms, data mining, and high-performance computing, are discussed. Explore future development directions such as artificial intelligence, cloud computing, and open source collaboration platforms, in order to provide new perspectives for researchers and promote further innovation and development in bioinformatics.

Bioinformatics; Big data; Machine learning; Genomics; High-performance computing