GPT-4, a language model, shows remarkable proficiency in annotating cell types in single-cell RNA-seq analysis, matching or outperforming human experts. Researchers at Columbia and Duke demonstrate GPT-4's ability to utilize marker gene information effectively.
Using GPTCelltype as the interface, GPT-4 is also notably faster, partly due to its utilization of differential genes from the standard single-cell analysis pipelines such as Seurat.
Assessing the performance of GPT-4, a highly potent large language model, for cell type annotation, and demonstrated that it can automatically and accurately annotate cell types by utilizing marker gene information generated from standard single-cell RNA-seq analysis pipelines.
GPTCelltype: Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis https://t.co/L9JbYqPUjp https://t.co/HUWmnoHCpv
Researchers at @Columbia and the @DukeMedSchool demonstrate that #GPT4, the fourth iteration of the #GenerativePretrainedTransformer series, has remarkable proficiency in annotating cell types using marker #gene information derived from #scRNAseq data. https://t.co/1M2j8xSc43
GPT-4 for identifying #cellTypes in #singleCells matches and sometimes outperforms expert methods @ColumbiaMSPH @naturemethods https://t.co/mE7so3qGu6
Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis https://t.co/KteTIKRmHC https://t.co/rtjytDRWIa 🧬🖥️ #rstats
A new study evaluates the performance of GPT-4 for single-cell type annotation. @ZhichengJi @HWenpin https://t.co/Fg6g3tafGN https://t.co/Ku4nIqHTNr
Wouldn't have predicted this one With no biology expertise, GPT-4 performs as good or better than human experts for single-cell RNA-seq cell annotation https://t.co/wKTWvXyzbT @naturemethods @HWenpin @ZhichengJi https://t.co/XVlgm9RoLt