MetaMuse: A Multi-Agent AI System for Biomedical Metadata Curation and Harmonization

Abstract

Inconsistent and unstructured metadata in public biomedical repositories, such as the Gene Expression Omnibus (GEO), severely limits data discoverability and research reproducibility. To address this, we introduce MO_SCPLOWETAC_SCPLOWMO_SCPLOWUSEC_SCPLOW, a modular, multi-agent artificial intelligence framework designed to autonomously extract, validate, and standardize unstructured biomedical metadata. Operating through a three-stage architecture utilizing large language model agents, specialized CO_SCPLOWURATORC_SCPLOWAO_SCPLOWGENTSC_SCPLOW contextually extract candidate values for specific target metadata fields. A centralized AO_SCPLOWRBITRATORC_SCPLOWAO_SCPLOWGENTC_SCPLOW enforces cross-field logical consistency to prevent contradictory annotations. Finally, a NO_SCPLOWORMALIZERC_SCPLOWAO_SCPLOWGENTC_SCPLOW leveraging a domain-specific semantic search model (SapBERT) maps these free-text candidates to formal ontological terms. We evaluated MO_SCPLOWETAC_SCPLOWMO_SCPLOWUSEC_SCPLOW on a gold-standard dataset of manually curated GEO samples, achieving over 95% curation accuracy across key target metadata fields, and demonstrated robust scalability on a broader dataset of 400 samples. Notably, MO_SCPLOWETAC_SCPLOWMO_SCPLOWUSEC_SCPLOW avoids data hallucination by defaulting to conservative false negatives when evidence is ambiguous, thereby preserving strict data integrity. By providing a fully auditable and context-aware curation pipeline, MO_SCPLOWETAC_SCPLOWMO_SCPLOWUSEC_SCPLOW offers a scalable solution for enriching public data repositories and accelerating reproducible, data-driven scientific discovery.