GEOAgent: AI Framework Automates Gene Expression Data Retrieval and Preprocessing
Researchers have developed GEOAgent, an AI-driven system that automatically retrieves and standardizes genomic datasets from the Gene Expression Omnibus (GEO), a major public repository. The framework uses natural language processing and automated workflows to handle heterogeneous metadata and assay-specific preprocessing that previously required manual curation. This addresses a significant bottleneck in genomic research by making large-scale dataset reuse more practical and efficient.
GEOAgent is an autonomous framework that combines semantic governance with an automated Nextflow pipeline called bioStream to solve the challenge of reusing genomic datasets at scale. The system was trained on metadata from 181,760 sequencing series and 84,756 associated PubMed records, organized in a relational database and semantic index to enable natural-language dataset queries. The framework automatically identifies assay types, resolves experimental design relationships, and standardizes sample naming conventions, significantly reducing manual curation work. In benchmarking tests, GEOAgent achieved 96% retrieval precision, 100% accuracy in assay classification, and 100% accuracy in sample relationship resolution. The system generates deployment-ready manifests that execute containerized workflows across both bulk and single-cell omics data types. The researchers have made the web platform, source code, and databases publicly available through GitHub and Zenodo.
What's missing
The study does not discuss potential limitations of the semantic indexing approach, such as performance on rare or novel assay types not well-represented in the training data, or how the framework handles datasets with incomplete or contradictory metadata annotations.
What different sources said
- bioRxivCenter
GEOAgent: An AI-driven Autonomous Framework for Intelligent GEO Data Retrieval and Standardized Preprocessing
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