ArticleNature communications2025
CASSIA: a multi-agent large language model for automated and interpretable cell annotation.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Trustworthy Agentic AI in Bioinformatics: From Workflow Automation to Traceable and Validated Biological Inference.Biology · 2026Review
- Agentic systems in computational pathology: architectures, evidence, and translational challenges.Journal of translational medicine · 2026Review
- Integrative evidence-knowledge marker selection enhances LLM-based cell type annotation in single-cell RNA-seq analysis.BioData mining · 2026Article
- MechAInistic: A Reviewer-Supervised Multi-Agent LLM System for Auditable Mechanistic Drug-Hypothesis Generation.bioRxiv : the preprint server for biology · 2026Article
- Explainable Agentic Artificial Intelligence in Healthcare: A Scoping Review.Bioengineering (Basel, Switzerland) · 2026Review
- Empowering AI data scientists using a multi-agent LLM framework with self-evolving capabilities for autonomous, tool-aware biomedical data analyses.Nature biomedical engineering · 2026Article
- Human Systems Immunology in the Omics Era: Challenges, Methods, and Emerging Directions.European journal of immunology · 2026Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Cell type annotation is an essential step in single-cell RNA-sequencing analysis, and numerous annotation methods are available. Most require a combination of computational and domain-specific expertise, and they frequently yield inconsistent results that can be challenging to interpret. Large language models have the potential to expand accessibility while reducing manual input and improving accuracy, but existing approaches suffer from hyperconfidence, hallucinations, and lack of reasoning. To address these limitations, we developed CASSIA for automated, accurate, and interpretable cell annotation of single-cell RNA-sequencing data. As demonstrated in analyses of 970 cell types, CASSIA improves annotation accuracy in benchmark datasets as well as complex and rare cell populations, and also provides users with reasoning and quality assessment to ensure interpretability, guard against hallucinations, and calibrate confidence.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.