Shudan Yan

Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, Zhejiang, China
Author    Correspondence author
Computational Molecular Biology, 2024, Vol. 14, No. 6   
Received: 07 Nov., 2024    Accepted: 08 Dec., 2024    Published: 20 Dec., 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.

With the rapid advancement of biological research, the growth of biological big data has reached unprecedented scale and complexity. This diversity and sheer volume of data present significant challenges in storage, management, and analysis, while simultaneously driving the rapid development of emerging data processing technologies. This study provides an overview of the latest progress in biological big data processing, covering topics from data preprocessing and cleaning techniques to efficient algorithms and computational frameworks, as well as the applications of artificial intelligence and machine learning in disease prediction, genomic analysis, and other fields. It further explores strategies and methods for multi-omics data integration and the implementation of scalable data visualization techniques in the analysis of biological networks and genomic data. Additionally, the article examines the potential applications of cutting-edge technologies such as quantum computing and edge computing in biological big data, along with the future development of automated data processing pipelines. The goal is to contribute to sustained innovation and progress in the field of biological data analysis.

Biological big data processing; Data preprocessing; Artificial intelligence; Multi-omics data Integration; Quantum computing