A data-intelligence-intensive bioinformatics copilot system for large-scale omics research and scientific insights

Area of Science:

• Bioinformatics
• Computational Biology
• Artificial Intelligence in Genomics

Background:

• High-throughput sequencing generates vast amounts of omics data, posing analysis challenges.
• Rapid AI development necessitates automated big biological data analysis and interdisciplinary insights.

Purpose of the Study:

• To propose a data-intelligence-intensive bioinformatics copilot (Bio-Copilot) system.
• To synergize AI and human researchers for hypothesis-free exploratory research in large-scale omics studies.
• To inspire novel scientific insights through collaborative AI-human intelligence.

Main Methods:

• Developed a Bio-Copilot system driven by large language models (LLMs) and human researchers.
• Implemented an agent group management strategy, human-agent interaction mechanisms, and a shared knowledge database.
• Employed continuous learning strategies for AI agents.

Main Results:

• Bio-Copilot achieved state-of-the-art performance across diverse bioinformatics tasks, outperforming leading AI agents like GPT-4o.
• Demonstrated exceptional task completeness in omics data analysis.
• Successfully applied to construct a human lung cell atlas, reproducing complex data integration and uncovering rare cell type characteristics.

Conclusions:

• Bio-Copilot effectively integrates AI with expert knowledge to accelerate impactful biological discoveries.
• The system has the potential to unravel hidden complexities in biological systems through advanced data analysis.
• Highlights the future of AI-assisted research in exploring uncharted scientific territories.