Title : From ontology fingerprints to BRAINCELL-AID: Literature-driven gene function and brain cell annotation
Abstract:
Biomedical literature contains a vast and rapidly expanding body of knowledge, making it both a valuable resource and a challenge to systematically extract information relevant to gene function and cellular identity. Our early works introduced Ontology Fingerprints, a framework that represents genes as interpretable embeddings derived from Gene Ontology terms enriched in literature. This approach captures gene functions through statistical associations while preserving interpretability, enabling applications such as gene prioritization and pathway discovery through a self-attention like mechanism. Building on this foundation, we extend literature-driven annotation to the cellular level using modern deep learning and large language models. We developed BRAINCELL-AID, an agentic AI framework that integrates fine-tuned LLMs, literature mining, and retrieval-augmented generation to annotate brain cell marker gene sets and cell types at scale. BRAINCELL-AID provides literature-grounded annotations for more than 20,000 gene sets and over 5,300 brain cell types, demonstrating strong cross-species performance and enabling biologically meaningful hypothesis generation. Together, this work establishes a continuum from knowledge-based embeddings to agentic AI systems, highlighting the role of literature-integrated representations in advancing gene function characterization, pathway discovery, and large-scale brain cell annotation.
