Qiang  Zhang , Yu Wang

Biotechnology Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China
Author    Correspondence author
Computational Molecular Biology, 2024, Vol. 14, No. 3   
Received: 08 Apr., 2024    Accepted: 23 May, 2024    Published: 10 Jun., 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 development of artificial intelligence (AI) and machine learning (ML) technologies, the field of biology, particularly genomic research, is undergoing profound transformations. This study explores how AI and ML are redefining genomic data analysis and functional genomics research, while emphasizing the critical role these technologies play in enhancing research efficiency, improving accuracy, and advancing personalized medicine. The application of AI in biology has expanded from basic data processing to complex tasks such as gene function prediction, identification of regulatory elements, and understanding epigenetic modifications. Through an in-depth analysis of key machine learning techniques, including supervised learning, unsupervised learning, and deep learning, this study demonstrates how these methods are revolutionizing traditional genomic data analysis workflows, significantly improving the efficiency of sequence alignment, variant calling, and gene expression profiling. Additionally, it discusses the future prospects of AI-driven genomic tools, cloud computing, big data integration, and open-source platform collaboration, aiming to provide valuable insights for future research and technological development.

Artificial intelligence (AI); Machine learning (ML); Genomic research; Functional genomics; Personalized medicine