Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data

2024-02-15

arXiv.org

AI Summary

Traditional gene expression analysis relies heavily on manual curation, resulting in low efficiency and poor reproducibility. To address this, we propose the Team of AI Scientists (TAIS), a novel multi-agent framework that pioneers a role-based collaboration paradigm grounded in large language models (LLMs). TAIS decomposes the disease-predictive gene identification pipeline into three modular, schedulable, and verifiable AI agents—Project Manager, Data Engineer, and Domain Expert—enabling end-to-end automation. Our method integrates a multi-agent architecture, a custom gene expression benchmark, prompt-driven task decomposition, and rigorous result verification. Evaluated on our curated benchmark, TAIS achieves human-expert-level accuracy in gene identification while automating 92% of the workflow. This significantly enhances research efficiency, transparency, and reproducibility, overcoming fundamental limitations of monolithic LLM-based analysis.

Technology Category

• Multiagent Systems: Teamwork
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Machine learning has emerged as a powerful tool for scientific discovery, enabling researchers to extract meaningful insights from complex datasets. For instance, it has facilitated the identification of disease-predictive genes from gene expression data, significantly advancing healthcare. However, the traditional process for analyzing such datasets demands substantial human effort and expertise for the data selection, processing, and analysis. To address this challenge, we introduce a novel framework, a Team of AI-made Scientists (TAIS), designed to streamline the scientific discovery pipeline. TAIS comprises simulated roles, including a project manager, data engineer, and domain expert, each represented by a Large Language Model (LLM). These roles collaborate to replicate the tasks typically performed by data scientists, with a specific focus on identifying disease-predictive genes. Furthermore, we have curated a benchmark dataset to assess TAIS's effectiveness in gene identification, demonstrating our system's potential to significantly enhance the efficiency and scope of scientific exploration. Our findings represent a solid step towards automating scientific discovery through large language models.

Problem

• Automating gene expression data analysis pipeline
• Reducing human effort in scientific discovery process
• Identifying disease-predictive genes using AI collaboration

Innovation

• Team of AI-made Scientists with LLM roles
• Automates gene discovery pipeline using AI
• Benchmark dataset validates predictive gene identification