Biological sequence analysis: Advances, medical applications, and challenges
With continuous developments in biotechnology and information technology, we are entering the era of biological sequence big data. Extracting valuable insights from large-scale data to decipher life activities presents a major challenge. Artificial intelligence, particularly big data analysis and natural language processing technologies, has emerged as a crucial tool in biological sequence analysis. These technologies facilitate pattern detection, offering new perspectives on complex biological processes. Notably, AlphaFold and ESM models have achieved significant strides in protein structure prediction and functional annotation. These breakthroughs not only accelerate fundamental biological research but also provide innovative tools and strategies for disease diagnosis and drug discovery. In this perspective, we discuss the advancements in biological sequence analysis and focus on their extensive medical applications. Additionally, we highlight relevant challenges and propose further directions, emphasizing the need for ongoing innovation in sequence analysis to fully realize its potential in biomedical research.
Authors
- Jiangyi Shao
- Bin Liu (ORCID: https://orcid.org/0000-0003-3685-9469)
- Hang Wei
Institutions
- Beijing Institute of Technology (CN)
- Xidian University (CN)
Publication Details
- Journal
- Fundamental Research
- Published
- 2024-12-27
- DOI
- https://doi.org/10.1016/j.fmre.2024.12.007
- Citations
- 1
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.23
Funders
- National Natural Science Foundation of China