AI for Scientific Discovery in Omics Data-Driven Precision Medicine.

Area of Science:

• Biomedical Research
• Computational Biology
• Genomics

Background:

• High-throughput technologies generate complex multi-omics data.
• Large language models (LLMs), foundation models (FMs), and AI agents are advancing scientific research.
• Integrating omics data with AI is crucial for modern biomedical discovery.

Purpose of the Study:

• To examine the intersection of large-scale omics datasets and AI models.
• To highlight emerging applications and challenges in this integrated field.
• To underscore the transformative impact on biomedical research and precision medicine.

Main Methods:

• Review of recent advancements in multi-omics data generation.
• Analysis of breakthroughs in large language models (LLMs), foundation models (FMs), and AI agents.
• Exploration of the integration strategies between omics data and AI.

Main Results:

• The convergence of omics data and AI is creating new opportunities in science.
• AI models enhance the analysis of complex biological systems.
• This integration is key to accelerating scientific discovery and precision medicine.

Conclusions:

• The synergy between massive omics datasets and AI is reshaping biomedical research.
• This integration promises to accelerate drug discovery and personalize treatments.
• Addressing challenges is vital for fully realizing the potential of AI in omics research.