Abstract
Motivation: Enrichment analysis is widely used to extract biological meanings from gene lists. However, the large number of available tools and reference libraries often make the interpretation of results challenging. Inexperienced users lack clear guidance on which datasets and terms are most relevant to their data, whereas experienced researchers must navigate large result sets and consult multiple resources to identify biologically relevant findings. Integrating AI into this process can help to bridge these gaps.
Results: We present Enrich.AI, a conversational biology assistant that integrates statistical enrichment analysis with automated biological interpretation. The agent operates on an LLM-based ReAct architecture and dynamically selects among the tools for enrichment (220+ gene set libraries), literature search (40+ million publications via Europe PMC), gene annotation, and database retrieval. Given a gene list and a short description of the biological context, Enrich.AI produces a written interpretation grounded in the retrieved literature and six interactive, configurable visualizations. A conversational interface enables human-in-the-loop refinement, letting researchers without programming experience explore results iteratively. The reliability of these interpretations was validated through a systematic evaluation on 50 gene sets, demonstrating high faithfulness to retrieved evidence and a low hallucination rate.