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

Cong Li; Meng Xiao; Pengfei Wang; Guihai Feng; Xin Li; Yuanchun Zhou

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

Genomics 2024-02-21 v1 Artificial Intelligence

Authors: Cong Li , Meng Xiao , Pengfei Wang , Guihai Feng , Xin Li , Yuanchun Zhou

Abstract

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.

Keywords

proteomics rna structure and analysis machine learning in genomics

Cite

@article{arxiv.2402.12405,
  title  = {scInterpreter: Training Large Language Models to Interpret scRNA-seq Data for Cell Type Annotation},
  author = {Cong Li and Meng Xiao and Pengfei Wang and Guihai Feng and Xin Li and Yuanchun Zhou},
  journal= {arXiv preprint arXiv:2402.12405},
  year   = {2024}
}

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4 pages, submitted to FCS

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

Abstract

Keywords

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