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.

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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

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article

Biological sequence analysis: Advances, medical applications, and challenges

Jiangyi Shao, Bin Liu, Hang Wei
1 citations
Fundamental Research
Machine Learning in Bioinformatics
0.23
article

Biological sequence analysis: Advances, medical applications, and challenges

Jiangyi Shao, Bin Liu, Hang Wei
article en
1 citations

Abstract

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.

Fundamental ResearchVol. 6(1)
Beijing Institute of Technology (CN), Xidian University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 40%
Machine Learning in Bioinformatics
0.23
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Biological sequence analysis: Advances, medical applications, and challenges — Jiangyi Shao, Bin Liu, et al. · Fundamental Research (2024) | TGRS Research Map | TGRS