# scInterpreter: Training Large Language Models to Interpret scRNA-seq Data for Cell Type Annotation

arXiv: https://arxiv.org/abs/2402.12405 · HTML: https://mlsanity.com/paper/2402.12405/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 18 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models](https://mlsanity.com/paper/2303.10420/) (2023)

## Built on by (most cited first, 5 of 5)

- [scAgent: Universal Single-Cell Annotation via a LLM Agent](https://mlsanity.com/paper/2504.04698/) (2025)
- [GeneSUM: Large Language Model-based Gene Summary Extraction](https://mlsanity.com/paper/2412.18154/) (2024)
- [Does your model understand genes? A benchmark of gene properties for biological and text models](https://mlsanity.com/paper/2412.04075/) (2024)
- [DiagnoLLM: A Hybrid Bayesian Neural Language Framework for Interpretable Disease Diagnosis](https://mlsanity.com/paper/2511.05810/) (2025)
- [SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes](https://mlsanity.com/paper/2507.04704/) (2025)

## Abstract (arXiv)

Despite the inherent limitations of existing Large Language Models in directly reading and interpreting single-cell omics data, they demonstrate significant potential and flexibility as the Foundation Model. This research focuses on how to train and adapt the Large Language Model with the capability to interpret and distinguish cell types in single-cell RNA sequencing data. Our preliminary research results indicate that these foundational models excel in accurately categorizing known cell types, demonstrating the potential of the Large Language Models as effective tools for uncovering new biological insights.