ArticleCommunications biology2025
Evaluation of cell type annotation reliability using a large language model-based identifier.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- celltypeEnrich: a consensus-based scRNA-seq cluster annotation tool.bioRxiv : the preprint server for biology · 2026Article
- Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.Human reproduction update · 2026Article
- Integrative evidence-knowledge marker selection enhances LLM-based cell type annotation in single-cell RNA-seq analysis.BioData mining · 2026Article
- CellTypeAI: cell annotation for scRNA-seq using local generative-AI.Bioinformatics (Oxford, England) · 2026Article
- Mathematical and Computational Models of Biochemical Reactions and Cell Signaling-From Ordinary Differential Equations to Machine Learning.International journal of molecular sciences · 2026Review
- Ambiguity-Aware Multi-Stage Cell-Type Annotation for Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Evaluation of cell type annotation reliability using a large language model-based identifier.Communications biology · 2025Article
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Authors and funding
11 authors.
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Abstract
Ensuring accurate cell type annotation in single-cell RNA sequencing data is a significant challenge, as both expert and automated methods can be biased or constrained by their training data, leading to errors and time-consuming revisions. To address this, we developed LICT (Large Language Model-based Identifier for Cell Types), a tool that leverages multi-model integration and a "talk-to-machine" approach. Validated across diverse datasets, LICT consistently aligns with expert annotations. With its objective framework for assessing annotation reliability, LICT can interpret cases where a single cell population exhibits multifaceted traits, allowing researchers to focus on the underlying biological insights. Comparisons with existing tools highlight LICT's superiority in efficiency, consistency, accuracy, and reliability, establishing it as a powerful tool for single-cell RNA sequencing analysis. Furthermore, its independence from reference data emphasizes LICT's generalizability, enhancing reproducibility and ensuring more reliable results in cellular research.
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Registered trials
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