Revisiting Gene Ontology Knowledge Discovery with Hierarchical Feature Selection and Virtual Study Group of AI Agents
Authors: Cen Wan, Alex A. Freitas
Organizations: School of Computing and Mathematical Sciences, Birkbeck, University of London, London, United Kingdom. · School of Computing, University of Kent, Canterbury, United Kingdom.
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.
Figures & tables
Figure 1: An example of hierarchical feature selection-based Gene Ontology knowledge discovery pipeline.
Figure 2: The hierarchical structure of the proposed AI agents-based virtual study group framework.
Figure 3: Further information regarding different LLM-based agents.