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IAN: An Intelligent System for Omics Data Analysis and Discovery.

Vijayaraj Nagarajan1, Guangpu Shi1, Reiko Horai1

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IAN, an AI-powered R package, integrates and analyzes omics data using a multi-agent system. It generates insightful biological interpretations, facilitating discovery while minimizing AI hallucination.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Artificial Intelligence in Genomics

Background:

  • High-throughput omics data analysis presents challenges in integration and interpretation.
  • Existing methods may lack comprehensive analytical and interpretive capabilities.

Purpose of the Study:

  • To introduce IAN, an R package designed for integrating, analyzing, and interpreting complex omics data.
  • To leverage a multi-agent artificial intelligence (AI) system for enhanced biological insights.

Main Methods:

  • Utilizes popular pathway and regulatory datasets (KEGG, WikiPathways, Reactome, GO, ChEA) and STRING for enrichment analysis.
  • Employs a large language model (LLM) within a multi-agent architecture to summarize and interpret enrichment results.
  • Applies carefully engineered prompts and grounding instructions for contextual integration and interpretation.

Main Results:

  • IAN successfully reanalyzes published omics datasets, demonstrating its potential for biological discovery.
  • The system shows remarkable performance in avoiding AI hallucination during data interpretation.
  • Provides insightful explanations, system overviews, identification of key regulators, and novel observations.

Conclusions:

  • IAN offers a powerful, AI-driven approach to facilitate biological discovery from complex omics data.
  • The multi-agent LLM architecture enhances the interpretation of enrichment analysis results.
  • The package provides a valuable tool for researchers in bioinformatics and computational biology.