scInterpreter: Training Large Language Models to Interpret scRNA-seq Data for Cell Type Annotation
CoRR(2024)
摘要
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.
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