Revisiting Gene Ontology Knowledge Discovery with Hierarchical Feature Selection and Virtual Study Group of AI Agents

2026-03-20

AI Summary

This study addresses the challenge of efficiently extracting aging-related biological knowledge from vast Gene Ontology (GO) data. To this end, we propose a novel framework that integrates a multi-agent virtual research team with hierarchical feature selection. The approach leverages large language model–driven agents to collaboratively generate and validate hypotheses, complemented by cross-species bioinformatic analyses to identify high-confidence aging-associated GO terms. By uniquely combining a virtual scientific team architecture with hierarchical feature selection, our method substantially enhances the interpretability and reliability of AI-driven biological discovery. Experimental validation across four model organisms successfully recapitulated numerous aging mechanisms well-supported in the literature, demonstrating the framework’s effectiveness and practical utility.

Technology Category

• Multiagent Systems: Teamwork
• Knowledge Representation and Reasoning: Ontologies
• Cognitive Modeling & Cognitive Systems: Agent Architectures

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
• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Large language models have achieved great success in multiple challenging tasks, and their capacity can be further boosted by the emerging agentic AI techniques. This new computing paradigm has already started revolutionising the traditional scientific discovery pipelines. In this work, we propose a novel agentic AI-based knowledge discovery-oriented virtual study group that aims to extract meaningful ageing-related biological knowledge considering highly ageing-related Gene Ontology terms that are selected by hierarchical feature selection methods. We investigate the performance of the proposed agentic AI framework by considering four different model organisms' ageing-related Gene Ontology terms and validate the biological findings by reviewing existing research articles. It is found that the majority of the AI agent-generated scientific claims can be supported by existing literatures and the proposed internal mechanisms of the virtual study group also play an important role in the designed agentic AI-based knowledge discovery framework.

Problem

• Gene Ontology
• knowledge discovery
• ageing
• biological knowledge extraction
• scientific discovery

Innovation

• agentic AI
• virtual study group
• hierarchical feature selection
• Gene Ontology
• knowledge discovery