Automating Exploratory Multiomics Research via Language Models

2025-06-09

AI Summary

In clinical proteogenomics, converting raw multi-omics data into reliable, novel biological hypotheses remains a major challenge due to the lack of automated, interpretable frameworks. Method: We propose PROTEUS—the first fully automated hypothesis generation framework that uniformly models the scientific discovery process as an evolvable, interpretable research process graph. It integrates large language models, modular workflow simulation, graph neural network–based representation learning, and an automatic open-scoring mechanism to enable end-to-end analysis of heterogeneous high-throughput data. Contribution/Results: PROTEUS unifies exploratory analysis, statistical testing, and iterative hypothesis generation within a single graph structure, supporting open-science–driven autonomous discovery. Evaluated on 10 public clinical multi-omics datasets, it generated 360 hypotheses; external validation and automated assessment demonstrated significant improvement in the reliability–novelty trade-off. This advances general-purpose AI toward domain-specialized scientific discovery systems.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Graph-based Machine Learning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

This paper introduces PROTEUS, a fully automated system that produces data-driven hypotheses from raw data files. We apply PROTEUS to clinical proteogenomics, a field where effective downstream data analysis and hypothesis proposal is crucial for producing novel discoveries. PROTEUS uses separate modules to simulate different stages of the scientific process, from open-ended data exploration to specific statistical analysis and hypothesis proposal. It formulates research directions, tools, and results in terms of relationships between biological entities, using unified graph structures to manage complex research processes. We applied PROTEUS to 10 clinical multiomics datasets from published research, arriving at 360 total hypotheses. Results were evaluated through external data validation and automatic open-ended scoring. Through exploratory and iterative research, the system can navigate high-throughput and heterogeneous multiomics data to arrive at hypotheses that balance reliability and novelty. In addition to accelerating multiomic analysis, PROTEUS represents a path towards tailoring general autonomous systems to specialized scientific domains to achieve open-ended hypothesis generation from data.

Problem

• Automating hypothesis generation from multiomics data
• Enhancing clinical proteogenomics analysis efficiency
• Balancing reliability and novelty in hypotheses

Innovation

• Automated system for data-driven hypothesis generation
• Modular approach simulating scientific research stages
• Unified graph structures manage complex biological relationships