ArticleNature biomedical engineering2025
Simple and effective embedding model for single-cell biology built from ChatGPT.
Article in Nature biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
- Mapping Gene Expression to an Interpretable Semantic Space.bioRxiv : the preprint server for biology · 2026Article
- scTransMIL bridges patient-level disease states and single-cell transcriptomics for cancer screening and heterogeneity inference.Nature communications · 2026Article
- Finetuning Foundation Models for Temporal Clinical Transcriptomics Data.Bioinformatics (Oxford, England) · 2026Article
- scProtoTransformer: Scalable reference mapping across molecules, cells, and donors.Science advances · 2026Article
- CoxFormer enables spatial omics inference with multimodal generative modeling.Nature communications · 2026Article
- An embedding-based framework enables statistical testing of gene-set function hypotheses inferred by large language models.Nature communications · 2026Article
- Mathematical and Computational Models of Biochemical Reactions and Cell Signaling-From Ordinary Differential Equations to Machine Learning.International journal of molecular sciences · 2026Review
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- scGenoByte: a GenoByte embedding transformer with biological priors for cell type annotation.Briefings in bioinformatics · 2026Article
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- Evaluating the role of pretraining dataset size and diversity on single-cell foundation model performance.Nature methods · 2026Article
- An AI-Powered Trisomy 21 Research Assistant.bioRxiv : the preprint server for biology · 2026Article
- Evaluating the learnability of single-cell large language models on multiple tasks.BMC genomics · 2026Article
- Enhancing Drug Response Prediction in Epilepsy with Emerging Multimodal Models: Focus on Clinical, Pharmacologic, and Genomic Factors.CNS drugs · 2026Review
- DNAwhisper: An Integrated Deep Learning Pyramidal Framework for Multi-Trait Genomic Prediction and Adaptive Marker Prioritisation.Plant biotechnology journal · 2026Article
- Lense: Optimizing data preprocessing in single-cell omics using LLMs.bioRxiv : the preprint server for biology · 2026Article
- Lense: optimizing data preprocessing in single-cell omics using large language models.Briefings in bioinformatics · 2026Article
- Cell-o1 : training LLMs to solve single-cell reasoning puzzles with reinforcement learning.Bioinformatics (Oxford, England) · 2026Article
- Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
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2 authors.
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Abstract
Large-scale gene-expression data are being leveraged to pretrain models that implicitly learn gene and cellular functions. However, such models require extensive data curation and training. Here we explore a much simpler alternative: leveraging ChatGPT embeddings of genes based on the literature. We used GPT-3.5 to generate gene embeddings from text descriptions of individual genes and to then generate single-cell embeddings by averaging the gene embeddings weighted by each gene's expression level. We also created a sentence embedding for each cell by using only the gene names ordered by their expression level. On many downstream tasks used to evaluate pretrained single-cell embedding models-particularly, tasks of gene-property and cell-type classifications-our model, which we named GenePT, achieved comparable or better performance than models pretrained from gene-expression profiles of millions of cells. GenePT shows that large-language-model embeddings of the literature provide a simple and effective path to encoding single-cell biological knowledge.
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