arXiv:2510.17064v4 Announce Type: replace Abstract: Single-cell RNA sequencing has transformed our ability to identify diverse cell types and their transcriptomic signatures. However, annotating these signatures-especially those involving poorly characterized genes-remains a major challenge. Traditional methods, such as Gene Set Enrichment Analysis (GSEA), depend on well-curated annotations and often perform poorly in these contexts. Large Language Models (LLMs) offer a promising alternative but struggle to represent complex biological knowledge within structured ontologies. To address this, w
The proliferation of single-cell RNA sequencing data creates an urgent need for more sophisticated annotation methods, while LLMs are now advanced enough to address these complex biological challenges.
This development can significantly accelerate biomedical research by improving the accuracy and efficiency of cell type identification, foundational for drug discovery and disease understanding.
Traditional gene annotation methods that rely on human-curated datasets are now complemented by agentic AI, which can autonomously generate and refine biological knowledge.
- · Biomedical Researchers
- · Pharmaceutical Companies
- · AI-driven Biotech Startups
- · Precision Medicine Providers
- · Manual Annotation Services
- · Outdated Bioinformatic Tools
More accurate and faster identification of novel cell types and their functions will accelerate basic biological discovery.
Improved cell type annotation will enhance target identification and drug repurposing efforts, leading to faster development of new therapies.
The integration of agentic AI in biological annotation could pave the way for fully autonomous scientific discovery pipelines, dramatically reducing research cycles.
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Read at arXiv cs.AI