ArticleNature methods2024
scGPT: toward building a foundation model for single-cell multi-omics using generative AI.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 704 papers.
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Who cites it
704 citing papers in PubMed.
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- Latent causal diffusions for single-cell perturbation modeling.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
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- VCCV: conservative transcriptomic corroboration for measurement prioritization of computational drug-target hypotheses.Bioinformatics (Oxford, England) · 2026Article
- ArchMap is a web-based platform for reference-based analysis of single-cell datasets.Nature genetics · 2026Article
- Deep learning perturbation models can outperform baselines on calibrated metrics.Nature biotechnology · 2026Article
- 3D multi-omics tumour atlases: from technology to biology and clinical translation.Nature reviews. Cancer · 2026Review
- Computational approaches for multimodal lineage tracing.Nature reviews. Genetics · 2026Review
- Predicting transcriptional regulators in plants in the era of artificial intelligence.Current opinion in plant biology · 2026Review
- scPlantAnnotate: an accurate and robust transformer-based model for plant cell type annotation.Journal of advanced research · 2026Article
- Multimodal learning enables chat-based exploration of single-cell data.Nature biotechnology · 2026Article
- Reconstructing signaling histories of single cells via perturbation screens and transfer learning.Nature methods · 2026Article
- Pop-Corn: Predicting Perturbation Phenotype Effects Across Single-Cell and Spatial Contexts.bioRxiv : the preprint server for biology · 2026Article
- CardioChrom Reconstructs the Human Cardiac Virtual Epigenome from Single-Nucleus Transcriptomes.bioRxiv : the preprint server for biology · 2026Article
- In Vivo Direct Reprogramming: Current Progress and Future Prospects from Mechanisms to Therapeutic Application.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
644 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically, the combination of large-scale diverse datasets and pretrained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between language and cellular biology (in which texts comprise words; similarly, cells are defined by genes), our study probes the applicability of foundation models to advance cellular biology and genetic research. Using burgeoning single-cell sequencing data, we have constructed a foundation model for single-cell biology, scGPT, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction and gene network inference.
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38409223What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.