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Progress and opportunities of foundation models in bioinformatics
Qing Li1, Zhihang Hu1, Yixuan Wang1
1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, 999077, China.
Briefings in Bioinformatics
|October 26, 2024
Summary
Foundation models (FMs) are revolutionizing bioinformatics by overcoming data limitations and noise. This survey explores their applications, challenges, and future directions in computational biology.
Area of Science:
- Bioinformatics
- Computational Biology
- Artificial Intelligence
Background:
- Bioinformatics faces challenges with limited annotated data and data noise.
- Foundation models (FMs) represent a significant advancement in artificial intelligence (AI) for biological research.
- FMs offer novel solutions for diverse biological data complexities.
Purpose of the Study:
- To survey and summarize the application of FMs in bioinformatics.
- To trace the evolution, current landscape, and methodologies of FMs in biology.
- To guide researchers in selecting appropriate FMs for specific biological tasks.
Main Methods:
- Investigating the evolutionary trajectory of FMs in bioinformatics.
- Analyzing the application of FMs to biological problems like sequence analysis, structure prediction, and function annotation.
- Contrasting FM architectures and advancements with conventional methods.
Main Results:
- FMs demonstrate efficacy in downstream validation tasks, representing diverse biological entities.
- FMs offer improved performance over conventional methods in various biological domains.
- Identified challenges include data noise, model interpretability, and potential biases in FM applications.
Conclusions:
- FMs are ushering in a new era in computational biology.
- Understanding FM limitations is crucial for optimal performance.
- This review provides a roadmap for future FM development and application in biological research.
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