scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI

Cui, Haotian; Wang, Chloe; Maan, Hassaan; Pang, Kuan; Luo, Fengning; Wang, Bo

doi:10.1101/2023.04.30.538439

PreprintCited by 1,405Single-cell and spatial transcriptomicsBioinformatics and Genomic NetworksGene Regulatory Network Analysis

scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI

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Cui, Haotian, Wang, Chloe, Maan, Hassaan, Pang, Kuan, Luo, Fengning, Wang, Bo·2023·Nature Methods

DOI 10.1101/2023.04.30.538439

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Abstract

Generative pre-trained models have achieved remarkable success in various domains such as natural language processing and computer vision. Specifically, the combination of large-scale diverse datasets and pre-trained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between linguistic constructs and cellular biology — where texts comprise words, similarly, cells are defined by genes — our study probes the applicability of foundation models to advance cellular biology and genetics research. Utilizing the burgeoning single-cell sequencing data, we have pioneered the construction of a foundation model for single-cell biology, scGPT, which is based on generative pre-trained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT, a generative pre-trained transformer, effectively distills critical biological insights concerning genes and cells. Through the 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, genetic perturbation prediction, and gene network inference. The scGPT codebase is publicly available at https://github.com/bowang-lab/scGPT .

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Empirical benchmarking studies have documented substantial performance degradation for biological foundation models under cross-platform transfer, preprocessing variability, and rare-population evaluation.
“Empirical benchmarking studies have documented substantial performance degradation for biological foundation models under cross-platform transfer, preprocessing variability, and rare-population evaluation [20–24].”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [20]
Section 2.2 reports lower annotation precision for rare cell types with extremely few cells in the reference set, supporting the rare-population part of the claim. The Methods discuss batch effects and preprocessing, but the passages found do not document substantial performance degradation caused by cross-platform transfer or preprocessing variability. Page metadata was not available for the decisive section. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Perturbation prediction offers experimentally generated model input/output with well-defined objectives, making it a more robust benchmark for assessing foundation models than tasks such as cell annotation and batch correction.
“Compared to tasks such as cell annotation and batch correction, perturbation prediction offers experimentally generated model input/output with well-defined objectives, making it a more robust benchmark for assessing foundation models (15, 20, 21).”
From A systematic comparison of single-cell perturbation response prediction models (2026) · cites this paper as [15]
Methods §4.5 describes control cells as inputs and experimentally perturbed cells as targets; §4.7 specifies metrics for evaluating predictions against measured expression. These passages support the claim that perturbation prediction has defined inputs, outputs and objectives, but the paper does not say it is a more robust foundation-model benchmark than cell annotation or batch correction. Page numbers were not supplied for these passages. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Geneformer and scGPT offer pretrained weights and have been trained using unsupervised objectives on extensive datasets (ca. 30M single-cell transcriptomes).
“We chose these models because they offer pretrained weights and have been trained using unsupervised objectives on extensive datasets (ca. 30M single-cell transcriptomes).”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
The Results (section 2.1, p. 3) state that scGPT was trained in a self-supervised manner on 33 million single-cell RNA-seq profiles, consistent with the approximate figure. The Supplementary Notes describe Geneformer as a pretrained transformer, but do not establish its training objective, dataset size, or availability of weights. The paper says scGPT's codebase is public, but the cited passages do not explicitly say its pretrained weights are offered. (LLM, conf=0.86)
Experimental — accuracy not yet measured
scGPT directly learns a cell embedding, which is integrated into its pretraining loss of predicting masked genes: scGPT first predicts a masked gene expression bin and a cell embedding from unmasked genes; then, in a second step, it further iteratively refines masked gene expression using the cell embedding predicted in the first step.
“However, scGPT directly learns a cell embedding, which is integrated into its pretraining loss of predicting masked genes: scGPT first predicts a masked gene expression bin and a cell embedding from unmasked genes; then, in a second step, it further iteratively refines masked gene expression using the cell embedding predicted in the first step.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
Recent advances incorporating graph-based methods and foundational models have improved generalizability.
“Recent advances have incorporated graph-based methods and foundational models that take advantage of prior knowledge of gene expression relationships, resulting in improved generalizability (14–17).”
From A systematic comparison of single-cell perturbation response prediction models (2026) · cites this paper as [15]
The Discussion (p. 17) says scGPT is a pretrained foundation model that generalizes, captures gene networks, and improves downstream performance through transfer learning. Results §2.5 (p. 12) says it learns gene–gene relationships from expression data, but does not describe incorporating a graph-based method or supplying those relationships as prior knowledge. This source supports the foundation-model and generalizability parts, not that more specific account of how the improvement was achieved. (LLM, conf=0.83)
Experimental — accuracy not yet measured

Recent work suggests that architectural choices, such as generative versus contrastive pretraining objectives, introduce distinct trade-offs between representation flexibility, interpretability, and robustness to experimental variability.
“Recent work suggests that architectural choices, such as generative versus contrastive pretraining objectives, introduce distinct trade-offs between representation flexibility, interpretability, and robustness to experimental variability [20–23, 36].”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [20]
Methods §4.3 (p. 21) describes generative pretraining as a flexible feature extractor, and the Discussion reports interpretable attention-based gene interactions. The paper also reports batch correction and robust learned features (§2.4), but it does not establish trade-offs between generative and contrastive pretraining objectives across flexibility, interpretability, and experimental variability; contrastive learning appears as a fine-tuning objective. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Foundation cell models are pre-trained on vast amounts (>10M examples) of unlabelled scRNA-seq data, with the aim that such large datasets allow the models to capture general principles of gene regulation and signalling.
“These models are pre-trained on vast amounts (> 10 M examples) of unlabelled scRNA-seq data, with the aim that such large datasets allow the models to capture general principles of gene regulation and signalling.”
From Benchmarking foundation cell models for post-perturbation RNA-seq prediction (2025) · cites this paper as [6]
In scGPT, the binned values and the gene tokens are separately embedded and summed in the embedding space, jointly representing the gene and its binned expression.
“Next, the binned values and the gene tokens (i.e., a unique index for each gene) are separately embedded and summed in the embedding space—jointly representing the gene and its binned expression.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
Foundation models for single-cell and multi-omic biology, such as scGPT, Nicheformer, and EpiAgent, produce high-quality continuous representations supporting cross-tissue transfer, perturbation inference, and regulatory feature learning.
“Foundation models for single-cell and multi-omic biology, such as scGPT [20], Nicheformer [21], and EpiAgent [22, 23], produce high-quality continuous representations of biological entities that support cross-tissue transfer, perturbation inference, and regulatory feature learning.”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [20]
scGPT is available in several variants, each pretrained on multiple different datasets.
“scGPT is available in several variants, each pretrained on multiple different datasets.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
Compared to Geneformer, scGPT has 3 times the number of parameters, using 12 Transformer layers with 8 attention heads.
“Finally, compared to Geneformer, scGPT has 3 the number of parameters, using 12 Transformer layers with 8 attention heads.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
scGPT preprocesses each gene expression vector by independently binning values into 50 equidistant bins where the lowest bin is the lowest expression and the highest bin corresponds to the highest expression.
“scGPT preprocesses each gene expression vector by independently binning values into 50 equidistant bins where the lowest bin is the lowest expression and the highest bin corresponds to the highest expression.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
Like Geneformer, scGPT uses an MLM task.
“Like Geneformer, scGPT uses an MLM task.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
To assess cell type distinctness, we employ metrics based on Average Silhouette Width (ASW) [25] and Average Bio (AvgBIO) scores [7].
“To assess this, we employ metrics based on Average Silhouette Width (ASW) [25] and Average Bio (AvgBIO) scores [7].”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
To evaluate the performance of scGPT in its pretraining objective, we used the mean squared error (MSE), as used by the original authors for the model's loss [7].
“To evaluate the performance of scGPT in its pretraining objective, we used the mean squared error (MSE), as used by the original authors for the model’s loss [7].”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
scGPT focuses on processing high-dimensional and sparse RNA data to capture complex relationships between cells, thereby supporting transfer learning across datasets and even across species.
“scGPT [14] focuses on processing these high-dimensional and sparse RNA data to capture complex relationships between cells, thereby supporting transfer learning across datasets and even across species.”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [14]
Recent foundational models such as scGPT, Nicheformer, and EpiAgent produce transferable representations across omic modalities.
“We first examine recent foundational models that produce transferable representations across omic modalities, such as scGPT, Nicheformer, and EpiAgent, and discuss their architectural choices, training regimes, and interpretability constraints.”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [20]
Empirical benchmarking studies have shown that performance improvements observed in standard evaluation settings can decrease substantially when models are tested across different laboratories, sequencing platforms, or preprocessing pipelines.
“Empirical benchmarking studies have shown that performance improvements observed in standard evaluation settings can decrease substantially when models are tested across different laboratories, sequencing platforms, or preprocessing pipelines [20–23].”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [20]
Single-cell foundation models and perturbation predictors support cellular-response forecasting.
“Single-cell foundation models and perturbation predictors support cellular-response forecasting [48-51].”
From RSI-Anything: Unlocking Recursive Self-Improvement with World Models (2026) · cites this paper as [48]
scGPT can encode complex interactions between perturbed and other genes.
“With the help of self-attention mechanisms over the gene dimension, scGPT can encode complex interactions between perturbed and other genes.”
From A mini-review on perturbation modelling across single-cell omic modalities (2024) · cites this paper as [69]
scGPT enables the model to learn from existing experimental data (Perturb-seq) through few-shot learning and make accurate predictions of gene expressions following perturbation for unseen experiments (OOD).
“This enables the model to learn from existing experimental data (i.e., Perturb-seq data) through few-shot learning and make accurate predictions of gene expressions following perturbation for unseen experiments (OOD).”
From A mini-review on perturbation modelling across single-cell omic modalities (2024) · cites this paper as [69]
Transformer-based methods support clinical text processing, whole-slide and radiologic image interpretation, and genomic sequence modeling in oncology.
“In oncology, transformer-based methods support clinical text processing, whole-slide and radiologic image interpretation (vision transformers), and genomic sequence modeling [24,25,26,27].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [27]
Both scGPT and Geneformer fine-tune their cell embeddings for cell type classification.
“Both scGPT and Geneformer fine-tune their cell embeddings for cell type classification.”
From Assessing the limits of zero-shot foundation models in single-cell biology (2023) · cites this paper as [6]
Most previous works, including scGPT and Geneformer, rely on fine-tuning to specialize task-specific models.
“To evaluate their models, most previous works-including scGPT and Geneformer-rely on fine-tuning to specialize task-specific models.”
From Assessing the limits of zero-shot foundation models in single-cell biology (2023) · cites this paper as [6]
Geneformer and scGPT offer pretrained weights and were trained using unsupervised objectives on extensive datasets (ca. 30M single-cell transcriptomes).
“We chose these models because they offer pretrained weights (whereas several other possible models did not have public weights at time of evaluation) and have been trained using unsupervised objectives on extensive datasets (ca. 30M single-cell transcriptomes).”
From Assessing the limits of zero-shot foundation models in single-cell biology (2023) · cites this paper as [6]

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Method
Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing
Cusanovich, D. A., Daza, R., Adey, A., Pliner, H. A., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C., & Shendure, J. (2015) · Science · doi:10.1126/science.aab1601 · Cited by 1,248 · cited 7× · see citing works
“10X Multiome PBMC The 10X Multiome PBMC dataset [47] contains paired single-cell RNA and ATAC data on human PBMC cells sequenced by the 10X Single Cell Multiome protocol.”
MethodInfluential
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Dao, T., Fu, D. Y., Ermon, S., Rudra, A., & Ré, C. (2022) · arXiv · doi:10.48550/arxiv.2205.14135 · Cited by 1,214 · cited 3× · see citing works
“To address this challenge and ensure efficient self-attention mechanisms, we leverage the accelerated self-attention implementation by Flash-Attention [65].”
Method
GSEApy: a comprehensive package for performing gene set enrichment analysis in Python
Fang, Z., Liu, X., & Peltz, G. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac757 · Cited by 1,209 · cited 1× · see citing works
“The 1048 pathway enrichment analysis was implemented using the GSEApy package [86].”
Method
A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response
Adamson, B., Norman, T. M., Jost, M., Cho, M. Y., Nuñez, J. K., Chen, Y., Villalta, J. E., Gilbert, L. A., Horlbeck, M. A., Hein, M. Y., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.048 · Cited by 1,193 · cited 8× · see citing works
“For the attention-based target gene selection, we fine-tuned the scGPT blood model on the 756 Adamson perturbation dataset that consists of 87 CRISPR interference experiments on a leukemia 757 cell line [36].”
BackgroundMethodInfluential
Transfer learning enables predictions in network biology
Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., et al. (2023) · Nature · doi:10.1038/s41586-023-06139-9 · Cited by 1,193 · see citing works
A benchmark of batch-effect correction methods for single-cell RNA sequencing data
Tran, H. T. N., Ang, K. S., Chevrier, M., Zhang, X., Lee, N. Y. S., Goh, M., & Chen, J. (2020) · Genome Biology · doi:10.1186/s13059-019-1850-9 · Cited by 1,113 · cited 1× · see citing works
“introduced by sequencing technologies [74, 75].”
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017) · Genome Medicine · doi:10.1186/s13073-017-0467-4 · Cited by 1,071 · see citing works
ATF6 Activated by Proteolysis Binds in the Presence of NF-Y (CBF) Directly to the <i>cis</i> -Acting Element Responsible for the Mammalian Unfolded Protein Response
Yoshida, H., Okada, T., Haze, K., Yanagi, H., Yura, T., Negishi, M., & Mori, K. (2000) · Molecular and Cellular Biology · doi:10.1128/mcb.20.18.6755-6767.2000 · Cited by 934 · cited 1× · see citing works
“Notably, the ATF6 pathway identified in DDIT3 -knockout setting is known to mediate 357 unfolded protein response and regulates cell apoptosis [57, 58].”
Background
Linformer: Self-Attention with Linear Complexity
Wang, S., Li, B. Z., Khabsa, M., Fang, H., & Ma, H. (2020) · arXiv · doi:10.48550/arxiv.2006.04768 · Cited by 867 · cited 1× · see citing works
“Although Flash-Attention was adopted, any 529 efficient transformers can be potentially utilized for scGPT as well, such as Transformers with 530 linear complexity (Linformer) [66] and Kernelized Self-Attention (KSA) [67].”
Method
Transcriptomic diversity of cell types across the adult human brain
Siletti, K., Hodge, R., Mossi Albiach, A., Lee, K. W., Ding, S.-L., Hu, L., Lönnerberg, P., Bakken, T., Casper, T., Clark, M., et al. (2023) · Science · doi:10.1126/science.add7046 · Cited by 832 · cited 8× · see citing works
“[45], which originally contained 606 high-quality samples encompassing 824 ten diverse brain regions.”
BackgroundMethodInfluential
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
Katharopoulos, A., Vyas, A., Pappas, N., & Fleuret, F. (2020) · arXiv · doi:10.48550/arxiv.2006.16236 · Cited by 827 · cited 2× · see citing works
“Although Flash-Attention was adopted, any efficient transformers can be potentially utilized for scGPT as well, such as Transformers with linear complexity (Linformer) [66] and Kernelized Self-Attention (KSA) [67].”
Method
Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A., & Murali, T. M. (2020) · Nature Methods · doi:10.1038/s41592-019-0690-6 · Cited by 792 · cited 1× · see citing works
“Existing GRN inference 295 methods often rely on correlation in static gene expression or pseudo-time estimates as a proxy for 296 causal graphs [50].”
Method
A Python library for probabilistic analysis of single-cell omics data
Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Valiollah Pour Amiri, V., Hong, J., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01206-w · Cited by 789 · see citing works
Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq
Replogle, J. M., Saunders, R. A., Pogson, A. N., Hussmann, J. A., Lenail, A., Guna, A., Mascibroda, L., Wagner, E. J., Adelman, K., Lithwick-Yanai, G., et al. (2022) · Cell · doi:10.1016/j.cell.2022.05.013 · Cited by 751 · see citing works
RNA Sequencing of Single Human Islet Cells Reveals Type 2 Diabetes Genes
Xin, Y., Kim, J., Okamoto, H., Ni, M., Wei, Y., Adler, C., Murphy, A. J., Yancopoulos, G. D., Lin, C., & Gromada, J. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.08.018 · Cited by 677 · cited 2× · see citing works
“The reference set consists of Braon[79] and Muraro[80] 804 datasets, and the query set consists of Xin[81], Segerstolpe[82], and Lawlor[83] datasets.”
Method
scGen predicts single-cell perturbation responses
Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-019-0494-8 · Cited by 661 · see citing works
Neuronal vulnerability and multilineage diversity in multiple sclerosis
Schirmer, L., Velmeshev, D., Holmqvist, S., Kaufmann, M., Werneburg, S., Jung, D., Vistnes, S., Stockley, J. H., Young, A., Steindel, M., et al. (2019) · Nature · doi:10.1038/s41586-019-1404-z · Cited by 638 · cited 1× · see citing works
“785 [78], is accessed from EMBL-EBI (https://www.”
scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data
Yang, F., Wang, W., Wang, F., Fang, Y., Tang, D., Huang, J., Lu, H., & Yao, J. (2021) · doi:10.1101/2021.12.05.471261 · Cited by 617 · see citing works
Multi-omics single-cell data integration and regulatory inference with graph-linked embedding
Cao, Z.-J., & Gao, G. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01284-4 · Cited by 616 · cited 7× · see citing works
“The processed data by Cao and Gao [14] contains 9,631 844 cells with read counts from 29,095 genes and 107,194 regions.”
BackgroundMethodInfluential
Cell-free gene expression: an expanded repertoire of applications
Silverman, A. D., Karim, A. S., & Jewett, M. C. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0186-3 · Cited by 602 · see citing works
Mapping single-cell data to reference atlases by transfer learning
Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Wagenstetter, M., Avsec, Ž., Gayoso, A., Yosef, N., Interlandi, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01001-7 · Cited by 602 · see citing works
Lineage tracing meets single-cell omics: opportunities and challenges
Wagner, D. E., & Klein, A. M. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0223-2 · Cited by 594 · see citing works
Single-cell transcriptomes identify human islet cell signatures and reveal cell-type–specific expression changes in type 2 diabetesPreprint
Lawlor, N. T., George, J., Bolisetty, M. T., Kursawe, R., Sun, L., Vijayakumar, S., Kycia, I., Robson, P., & Stitzel, M. L. (2016) · Genome Research · doi:10.1101/gr.212720.116 · Cited by 540 · cited 2× · see citing works
“The reference set consists of Braon[79] and Muraro[80] 804 datasets, and the query set consists of Xin[81], Segerstolpe[82], and Lawlor[83] datasets.”
Method
Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells
Mimitou, E. P., Lareau, C. A., Chen, K. Y., Zorzetto-Fernandes, A. L., Hao, Y., Takeshima, Y., Luo, W., Huang, T.-S., Yeung, B. Z., Papalexi, E., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00927-2 · Cited by 529 · cited 8× · see citing works
“In the mosaic data inte957 gration experiment, we benchmarked scGPT with scMoMat [15] on the ASAP PBMC [49] dataset.”
BackgroundMethodInfluential
The HLA system: Genetics, immunology, clinical testing, and clinical implications
Choo, S. Y. (2007) · Yonsei Medical Journal · doi:10.3349/ymj.2007.48.1.11 · Cited by 446 · see citing works
Immunobiology: The immune system in health and disease
Norman, P. S. (1995) · Journal of Allergy and Clinical Immunology · doi:10.1016/s0091-6749(95)70025-0 · Cited by 432 · cited 1× · see citing works
“The pre-trained scGPT model successfully identifies the group of 315 genes (CD3E, D, and G) encoding the T3 complex for T-cell activation, as well as CD79A and 316 CD79B for B-cell signaling, and CD8A and CD8B as co-receptors for HLA class I molecules [52] 317 (Figure 5B).”
Method
Exploring genetic interaction manifolds constructed from rich single-cell phenotypes
Norman, T. M., Horlbeck, M. A., Replogle, J. M., Ge, A. Y., Xu, A., Jost, M., Gilbert, L. A., & Weissman, J. S. (2019) · Science · doi:10.1126/science.aax4438 · Cited by 392 · cited 9× · see citing works
“For the perturbation prediction task, we evaluated our model using two perturbation datasets of 171 K562 leukemia cell lines: the Adamson Pertub-seq dataset [36] consisting of 87 one-gene perturba172 tions and the Norman Perturb-Seq dataset [37] consisting of 131 two-gene perturbations and 105 173 one-gene perturbatio…”
BackgroundMethodInfluential
Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling
Zhang, A. W., O’Flanagan, C. H., Chavez, E. A., Lim, J. L., Ceglia, N., McPherson, A., Wiens, M., Walters, P., Chan, T., Hewitson, B., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0529-1 · Cited by 373 · cited 1× · see citing works
“While several methods have 122 been proposed for cell annotation [30, 31, 32], they often require dimension reduction prior to 123 model input, which can lead to information loss.”
Method
Predicting transcriptional outcomes of novel multigene perturbations with GEARS
Roohani, Y., Huang, K., & Leskovec, J. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01905-6 · Cited by 355 · see citing works
ChIP-Atlas 2021 update: a data-mining suite for exploring epigenomic landscapes by fully integrating ChIP-seq, ATAC-seq and Bisulfite-seq data
Zou, Z., Ohta, T., Miura, F., & Oki, S. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac199 · Cited by 350 · cited 2× · see citing works
“We validated scGPT’s attention-based most influenced gene selection method in the ChIP994 Atlas database [56] which contains experiment-validated gene targets for known transcription fac995 tors.”
BackgroundMethod
Predicting cellular responses to complex perturbations in high‐throughput screens
Lotfollahi, M., Klimovskaia Susmelj, A., De Donno, C., Hetzel, L., Ji, Y., Ibarra, I. L., Srivatsan, S. R., Naghipourfar, M., Daza, R. M., Martin, B., et al. (2023) · Molecular Systems Biology · doi:10.15252/msb.202211517 · Cited by 307 · cited 4× · see citing works
“We conducted a performance comparison between scGPT and two other recent methods, GEARS [38] and CPA [39].”
MethodInfluential
CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing
de Kanter, J. K., Lijnzaad, P., Candelli, T., Margaritis, T., & Holstege, F. C. P. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz543 · Cited by 274 · cited 1× · see citing works
“While several methods have 122 been proposed for cell annotation [30, 31, 32], they often require dimension reduction prior to 123 model input, which can lead to information loss.”
Method
Characterizing cis-regulatory elements using single-cell epigenomics
Preissl, S., Gaulton, K. J., & Ren, B. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-022-00509-1 · Cited by 251 · see citing works
Multi-omics integration in the age of million single-cell data
Miao, Z., Humphreys, B. D., McMahon, A. P., & Kim, J. (2021) · Nature Reviews Nephrology · doi:10.1038/s41581-021-00463-x · Cited by 249 · cited 1× · see citing works
“Single-cell multi-omic (scMultiomic) data, which combines multiple views of genetic regulation such 258 as epigenetic, transcriptomic, and translation activities, presents a unique challenge in aggregating 259 cell representations while preserving biological signals [9, 10].”
Background
Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis
Sarkar, A., & Stephens, M. (2021) · Nature Genetics · doi:10.1038/s41588-021-00873-4 · Cited by 224 · cited 1× · see citing works
“A fundamental challenge in gene expression modelling is the vari483 ability in absolute magnitudes across different sequencing protocols [61].”
Background
Single cells make big data: New challenges and opportunities in transcriptomics
Angerer, P., Simon, L. M., Tritschler, S., Wolf, F., Fischer, D. S., & Theis, F. J. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.07.004 · Cited by 213 · see citing works
Jointly defining cell types from multiple single-cell datasets using LIGER
Liu, J., Gao, C., Sodicoff, J., Kozareva, V., Macosko, E. Z., & Welch, J. D. (2020) · Nature Protocols · doi:10.1038/s41596-020-0391-8 · Cited by 203 · see citing works
Temporal modelling using single-cell transcriptomics
Ding, J., Sharon, N., & Bar-Joseph, Z. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00444-7 · Cited by 195 · see citing works
Transformer for one stop interpretable cell type annotation
Chen, J., Xu, H., Tao, W., Chen, Z., Zhao, Y., & Han, J.-D. J. (2023) · Nature Communications · doi:10.1038/s41467-023-35923-4 · Cited by 189 · cited 5× · see citing works
“scRNA-seq cell type annotation We benchmarked scGPT against two recent transformer861 based cell type annotation methods, scBert [35] and TOSICA [34] on the Mye.”
MethodInfluential
Fast, sensitive, and accurate integration of single cell data with HarmonyPreprint
Korsunsky, I., Fan, J., Slowikowski, K., et al. (2018) · bioRxiv · doi:10.1101/461954 · Cited by 177 · see citing works
Machine learning for perturbational single-cell omics
Ji, Y., Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.016 · Cited by 137 · cited 1× · see citing works
“However, current 47 machine-learning-based methods in single-cell research are rather scattered, with specific models 48 dedicated to distinct analysis tasks [24, 25, 26].”
ATF6 Is a Critical Determinant of CHOP Dynamics during the Unfolded Protein Response
Yang, H., Niemeijer, M., van de Water, B., & Beltman, J. B. (2020) · iScience · doi:10.1016/j.isci.2020.100860 · Cited by 136 · cited 1× · see citing works
“Notably, the ATF6 pathway identified in DDIT3 -knockout setting is known to mediate 357 unfolded protein response and regulates cell apoptosis [57, 58].”
Background
EpiScanpy: integrated single-cell epigenomic analysis
Danese, A., Richter, M. L., Chaichoompu, K., Fischer, D. S., Theis, F. J., & Colomé‐Tatché, M. (2021) · Nature Communications · doi:10.1038/s41467-021-25131-3 · Cited by 131 · see citing works
Benchmarking atlas-level data integration in single-cell genomicsPreprint
Luecken, M. D., Büttner, M., Chaichoompu, K., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.22.111161 · Cited by 122 · see citing works
The effect of eviction moratoria on the transmission of SARS-CoV-2
Nande, A., Sheen, J. K., Walters, E. L., Klein, B., Chinazzi, M., Gheorghe, A. H., Adlam, B., Shinnick, J. E., Tejeda, M. F., Scarpino, S. V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22521-5 · Cited by 98 · see citing works
Transcriptomic diversity of cell types across the adult human brainPreprint
Siletti, K., Hodge, R. D., Albiach, A. M., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.12.511898 · Cited by 97 · see citing works
EpiScanpy: integrated single-cell epigenomic analysisPreprint
Danese, A., Richter, M., Fischer, D. S., Theis, F. J., & Colomé-Tatché, M. (2019) · bioRxiv · doi:10.1101/648097 · Cited by 88 · cited 1× · see citing works
“We used the EpiScanpy python 1043 library [85] on chromatin accessibility data for highly variable peak selection.”
Method
Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges
Liu, J., Fan, Z., Zhao, W., & Zhou, X. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.655536 · Cited by 84 · cited 1× · see citing works
“However, current 47 machine-learning-based methods in single-cell research are rather scattered, with specific models 48 dedicated to distinct analysis tasks [24, 25, 26].”
A sandbox for prediction and integration of DNA, RNA, and proteins in single cells
Luecken, M. D., Burkhardt, D. B., Cannoodt, R., et al. (2021) · Ghent University Academic Bibliography (Ghent University) · Cited by 84 · see citing works
scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection
Zhang, Z., Sun, H., Mariappan, R., Chen, X. X., Chen, X., Jain, M. S., Efremova, M., Teichmann, S. A., Rajan, V., & Zhang, X. (2023) · Nature Communications · doi:10.1038/s41467-023-36066-2 · Cited by 60 · cited 5× · see citing works
“In the mosaic data inte957 gration experiment, we benchmarked scGPT with scMoMat [15] on the ASAP PBMC [49] dataset.”
BackgroundMethodInfluential
Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic dataPreprint
Pratapa, A., Jalihal, A. P., Law, J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/642926 · Cited by 55 · see citing works
Learning interpretable cellular responses to complex perturbations in high-throughput screensPreprint
Lotfollahi, M., Klimovskaia, A., Donno, C. D., Ji, Y., Ibarra, I. L., Wolf, F. A., Yakubova, N., Theis, F. J., & López-Paz, D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.14.439903 · Cited by 49 · cited 5× · see citing works
“We conducted a performance comparison between scGPT and two other recent methods, GEARS [38] and CPA [39].”
BackgroundMethodInfluential
Major histocompatibility complex: Antigen processing and presentation
Cruz‐Tapias, P., Castiblanco, J., & Anaya, J. (2013) · Cited by 49 · see citing works
GEARS: Predicting transcriptional outcomes of novel multi-gene perturbationsPreprint
Roohani, Y., Huang, K., & Leskovec, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.12.499735 · Cited by 40 · cited 7× · see citing works
“We conducted a performance comparison between scGPT and two 182 other recent methods, GEARS [38] and CPA [39].”
MethodInfluential
CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencingPreprint
de Kanter, J. K., Lijnzaad, P., Candelli, T., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/558908 · Cited by 36 · see citing works
Guided Similarity Separation for Image Retrieval
Liu, C., Yu, G. W., Volkovs, M., et al. (2019) · neural information processing systems · Cited by 36 · see citing works
Single-cell RNA-seq analysis software providers scramble to offer solutions
Eisenstein, M. S. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0449-8 · Cited by 35 · see citing works
Algorithmic advances in machine learning for single-cell expression analysis
Moreno, S. O., Kloiber, K., Machart, P., & Bonn, S. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.02.002 · Cited by 35 · cited 1× · see citing works
“However, current 47 machine-learning-based methods in single-cell research are rather scattered, with specific models 48 dedicated to distinct analysis tasks [24, 25, 26].”
Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysisPreprint
Sarkar, A., & Stephens, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.07.030007 · Cited by 35 · see citing works
Jointly Defining Cell Types from Multiple Single-Cell Datasets Using LIGERPreprint
Liu, J., Gao, C., Sodicoff, J., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.07.029546 · Cited by 21 · see citing works
MultiCPA: Multimodal Compositional Perturbation Autoencoder
Inecik, K., Uhlmann, A., Lotfollahi, M., & Theis, F. (2022) · doi:10.1101/2022.07.08.499049 · Cited by 16 · see citing works
DeepMAPS: Single-cell biological network inference using heterogeneous graph transformerPreprint
Ma, A., Wang, X., Wang, C., Li, J., Xiao, T., Wang, J., Li, Y., Liu, Y.-T., Chang, Y., Wang, D., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.31.466658 · Cited by 14 · see citing works
Self-citation
One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data
Wang, C. X., Zhang, L., & Wang, B. (2022) · Genome biology · doi:10.1186/s13059-022-02659-1 · Cited by 14 · see citing works
Self-citation
Identification of transcriptional programs using dense vector representations defined by mutual information with GeneVector
Ceglia, N., Sethna, Z. M., Freeman, S. S., Uhlitz, F., Bojilova, V., Rusk, N., Burman, B., Chow, A. T., Salehi, S., Kabeer, F., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39985-2 · Cited by 9 · see citing works
Generative pretraining from large-scale transcriptomes: Implications for single-cell deciphering and clinical translationPreprint
Shen, H., Shen, X., Hu, J., Liu, J., Zhang, C., Wu, D., Feng, M., Yang, M., Li, Y., Yang, Y., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.31.478596 · Cited by 8 · see citing works
GeneVector: Identification of transcriptional programs using dense vector representations defined by mutual informationPreprint
Ceglia, N., Sethna, Z. M., Freeman, S. S., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.22.487554 · Cited by 5 · cited 1× · see citing works
“[77]’s pipelines, we further perform Leiden [40] clustering on the 754 similarity graph and extracted gene programs from gene clusters that consist of five or more genes.”
Method
A single-cell gene expression language modelPreprint
Connell, W., Khan, U., & Keiser, M. J. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.14330 · Cited by 3 · see citing works
Processed datasets used in the scGPT foundation model
Wang, C. (2024) · Figshare · doi:10.6084/m9.figshare.24954519 · Cited by 1 · see citing works
Self-citation
scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI
Cui, H., Wang, C., & Pang, K. (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.10466117 · Cited by 1 · see citing works
Self-citation
GPT-4 Technical Report
OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.08774
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805

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2019, In: Chinese Computational Linguistics: 18th China National Conference, CCL 2019, Kunming, China, October 18–20, 2019, Proceedings, 18, 194
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Sorted by citations

Large-scale foundation model on single-cell transcriptomics
Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Zhang, X., & Song, L. (2024) · Nature Methods · doi:10.1038/s41592-024-02305-7 · Cited by 628 · see citing works
CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data
Program, C. C. S., Abdulla, S., Aevermann, B., Assis, P., Badajoz, S., Bell, S. M., Bezzi, E., Cakir, B., Chaffer, J., Chambers, S., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1142 · Cited by 399 · see citing works
How to build the virtual cell with artificial intelligence: Priorities and opportunities
Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.015 · Cited by 393 · see citing works
Self-citation
Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics
Gulati, G. S., D'Silva, J. P., Liu, Y., Wang, L., & Newman, A. M. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00768-2 · Cited by 328 · see citing works
Deep learning-based approaches for multi-omics data integration and analysis
Ballard, J. L., Wang, Z., Li, W., Shen, L., & Long, Q. (2024) · BioData Mining · doi:10.1186/s13040-024-00391-z · Cited by 287 · see citing works
“Recently, Cui et al. [52] proposed scGPT, a foundation model for single-cell omics data.”
Background
A Vision-Language Foundation Model for Precision Oncology
Xiang, J., Wang, X., Zhang, X., Xi, Y., Eweje, F., Chen, Y., Li, Y., Bergstrom, C., Gopaulchan, M., Kim, T., et al. (2025) · Nature · doi:10.1038/s41586-024-08378-w · Cited by 256 · see citing works
Generative artificial intelligence empowers digital twins in drug discovery and clinical trials
Bordukova, M., Makarov, N., Rodriguez-Esteban, R., Schmich, F., & Menden, M. P. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2023.2273839 · Cited by 235 · see citing works
A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches
Baião, A. R., Cai, Z., Poulos, R. C., Robinson, P. J., Reddel, R. R., Zhong, Q., Vinga, S., & Gonçalves, E. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf355 · Cited by 249 · see citing works
The diversification of methods for studying cell–cell interactions and communication
Armingol, E., Baghdassarian, H., & Lewis, N. E. (2024) · Nature Reviews Genetics · doi:10.1038/s41576-023-00685-8 · Cited by 209 · see citing works
“Although these technologies have been reviewed in detail 21–26 , here we highlight examples that are pertinent to gaining biological insights about CCIs, and could help refine the aforementioned computational tools and validate their predictions, improving the fidelity of CCI inference.”
Advances in molecular pathology and therapy of non-small cell lung cancer
Huang, Q., Li, Y., Huang, Y., Wu, J., Bao, W., Xue, C., Li, X., Dong, S., Dong, Z., & Hu, S. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02243-6 · Cited by 197 · see citing works
AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships
Wu, Y., & Xie, L. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.030 · Cited by 205 · see citing works
Decoding the tumor microenvironment with spatial technologies
Walsh, L. A., & Quail, D. F. (2023) · Nature Immunology · doi:10.1038/s41590-023-01678-9 · Cited by 191 · see citing works
Transformers in single-cell omics: a review and new perspectives
Szałata, A., Hrovatin, K., Becker, S., Tejada-Lapuerta, A., Cui, H., Wang, B., & Theis, F. J. (2024) · Nature Methods · doi:10.1038/s41592-024-02353-z · Cited by 176 · see citing works
Self-citation
The Human Cell Atlas from a cell census to a unified foundation model
Rood, J., Wynne, S., Robson, L., Hupalowska, A., Randell, J., Teichmann, S. A., & Regev, A. (2024) · Nature · doi:10.1038/s41586-024-08338-4 · Cited by 168 · see citing works
Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future ProspectsPreprint
Hadi, M. U., Al-Tashi, Q., Qureshi, R., Shah, A., Muneer, A., Irfan, M., Zafar, A., Shaikh, M. B., Akhtar, N., Hassan, S. Z., et al. (2023) · doi:10.36227/techrxiv.23589741.v4 · Cited by 173 · see citing works
Universal Cell Embeddings: A Foundation Model for Cell BiologyPreprint
Rosen, Y., Roohani, Y., Agrawal, A., Samotorcan, L., Quake, S. R., & Leskovec, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.28.568918 · Cited by 165 · see citing works
“In particular, we compare against Geneformer [28] and scGPT [29].”
Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines
Ahlmann-Eltze, C., Huber, W., & Anders, S. (2025) · Nature Methods · doi:10.1038/s41592-025-02772-6 · Cited by 238 · see citing works
GeneCompass: Deciphering Universal Gene Regulatory Mechanisms with Knowledge-Informed Cross-Species Foundation Model
Yang, X., Liu, G., Feng, G., Bu, D., Wang, P., Jiang, J., Chen, S., Yang, Q., Zhang, Y., Man, Z., et al. (2023) · doi:10.1101/2023.09.26.559542 · Cited by 148 · see citing works
GeneCompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model
Yang, X., Liu, G., Feng, G., Bu, D., Wang, P., Jiang, J., Chen, S., Yang, Q., Miao, H., Zhang, Y., et al. (2024) · Cell Research · doi:10.1038/s41422-024-01034-y · Cited by 148 · see citing works
“To test whether the similarity between homologs was derived from the prior knowledge or the self-supervised pre-training, we compared the cosine similarity of homologous genes in various scenarios, including pre-trained GeneCompass with prior knowledge, Fig.”
Self-citation
CZ CELL×GENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data
Program, C. S.-C. B., Abdulla, S., Aevermann, B., Assis, P., Badajoz, S., Bell, S. M., Bezzi, E., Cakir, B., Chaffer, J., Chambers, S., et al. (2023) · doi:10.1101/2023.10.30.563174 · Cited by 142 · see citing works
Generative-AI-Driven Human Digital Twin in IoT Healthcare: A Comprehensive Survey
Chen, J., Shi, Y., Yi, C., Du, H., Kang, J., & Niyato, D. T. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3421918 · Cited by 155 · see citing works
“The model further leveraged attention mechanism to capture intricate gene-to-gene interactions at the single-cell level [52].”
Background
A cell atlas foundation model for scalable search of similar human cells
Heimberg, G., Kuo, T. C.-Y., DePianto, D. J., Salem, O., Heigl, T., Diamant, N., Scalia, G., Biancalani, T., Turley, S. J., Rock, J. R., et al. (2024) · Nature · doi:10.1038/s41586-024-08411-y · Cited by 179 · see citing works
Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas
Rood, J. E., Hupalowska, A., & Regev, A. (2024) · Cell · doi:10.1016/j.cell.2024.07.035 · Cited by 139 · see citing works
Towards multimodal foundation models in molecular cell biology
Cui, H., Tejada-Lapuerta, A., Brbić, M., Sáez-Rodríguez, J., Cristea, S., Goodarzi, H., Lotfollahi, M., Theis, F. J., & Wang, B. (2025) · Nature · doi:10.1038/s41586-025-08710-y · Cited by 131 · see citing works
“Recently, models such as scGPT 45 , CellOracle 74 , Geneformer 46 , CellOT 75 , CPA 38 , chemcCPA 76 and GEARS 39 have shown initial success in perturbing learned cell embeddings to predict resulting expression profiles.”
InfluentialSelf-citation
A foundation model of transcription across human cell types
(2025) · doi:10.1038/s41586-024-08391-z · Cited by 130 · see citing works
Foundation Model for Advancing Healthcare: Challenges, Opportunities and Future Directions
He, Y., Huang, F., Jiang, X., Nie, Y., Wang, M., Wang, J., & Chen, H. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3496744 · Cited by 130 · see citing works
Fostering AI Literacy in Elementary Science, Technology, Engineering, Art, and Mathematics (STEAM) Education in the Age of Generative AI
Relmasira, S. C., Lai, Y. C., & Donaldson, J. P. (2023) · Sustainability · doi:10.3390/su151813595 · Cited by 115 · see citing works
Large Scale Foundation Model on Single-cell Transcriptomics
Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Song, L., & Zhang, X. (2023) · doi:10.1101/2023.05.29.542705 · Cited by 639 · see citing works
Foundation models in bioinformatics
Guo, F., Guan, R., Li, Y., Liu, Q., Wang, X., Yang, C., & Wang, J. (2025) · National Science Review · doi:10.1093/nsr/nwaf028 · Cited by 121 · see citing works
A visual–omics foundation model to bridge histopathology with spatial transcriptomics
Chen, W., Zhang, P., Tran, T. N., Xiao, Y., Li, S., Shah, V. V., Cheng, H., Brannan, K. W., Youker, K., Lai, L., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02707-1 · Cited by 118 · see citing works
“Unlike single-cell foundation models like scGPT 57 , Geneformer 26 and scFoundation 27 , our approach models omics data as text, effectively bridging molecular and visual modalities.”
A foundational large language model for edible plant genomes
Mendoza‐Revilla, J., Trop, E., Gonzalez, L., Roller, M., Dalla-Torre, H., de Almeida, B. P., Richard, G., Caton, J., Carranza, N. L., Skwark, M. J., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06465-2 · Cited by 122 · see citing works
“We have provided the predicted impact of all possible single-nucleotide substitutions across all enhancer and promoter sequences for future research purposes.”
Background
GenePT: A Simple But Effective Foundation Model for Genes and Cells Built From ChatGPTPreprint
Chen, Y. T., & Zou, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.16.562533 · Cited by 106 · see citing works
“Our validation process follows that of Cui et al. [2] and consists of the following steps: 1. constructing gene networks based on the cosine similarities among the highly variable genes; 2. applying unsupervised Louvain clustering [38] to derive gene programs; and 3. qualitatively comparing the…”
Influential
Single-cell multi-omics in cancer immunotherapy: from tumor heterogeneity to personalized precision treatment
Le, J., Dian, Y., Zhao, D., Guo, Z., Luo, Z., Chen, X., Zeng, F., & Deng, G. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02426-3 · Cited by 109 · see citing works
Zero-shot evaluation reveals limitations of single-cell foundation models
Kedzierska, K. Z., Crawford, L., Amini, A. P., & Lu, A. X. (2025) · Genome Biology · doi:10.1186/s13059-025-03574-x · Cited by 109 · see citing works
“To assess this, we employ metrics based on Average Silhouette Width (ASW) [25] and Average Bio (AvgBIO) scores [7].”
Influential
Artificial intelligence applied to ‘omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment
Ghosh, S., Zhao, X., Alim, M., Brudno, M., & Bhat, M. (2024) · Gut · doi:10.1136/gutjnl-2023-331740 · Cited by 101 · see citing works
Transformers and genome language models
Consens, M. E., Dufault, C., Wainberg, M., Forster, D., Karimzadeh, M., Goodarzi, H., Theis, F. J., Moses, A. M., & Wang, B. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01007-9 · Cited by 148 · see citing works
“While this Review focuses on DNA-based genome language models, single-cell RNA-seq language models follow many similar principles 40,41,132–136 .”
Self-citation
Exploring the role of large language models in the scientific method: from hypothesis to discovery
Zhang, Y., Khan, S. A., Mahmud, A., Yang, H., Lavin, A., Levin, M. G., Frey, J. G., Dunnmon, J., Evans, J., Bundy, A., et al. (2025) · npj Artificial Intelligence · doi:10.1038/s44387-025-00019-5 · Cited by 109 · see citing works
A Comprehensive Survey of Foundation Models in Medicine
Khan, W., Leem, S., See, K. B., Wong, J. K., Zhang, S., & Fang, R. (2025) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2025.3531360 · Cited by 107 · see citing works
“5fromdataof 33,000genesobtainedfromGeneformer[91]andscGPT[92].”
Influential
Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development
Qiu, X., Li, H., Steeg, G. V., & Godzik, A. (2024) · Biomolecules · doi:10.3390/biom14030339 · Cited by 100 · see citing works
“In early 2020, Exscientia reported the initiation of Phase 1 clinical trials for DSP-1181, a compound designed to address obsessive-compulsive disorder.”
Background
The Large Language Models on Biomedical Data Analysis: A Survey
Lan, W., Tang, Z., Liu, M., Chen, Q., Peng, W., Chen, Y. P., & Pan, Y. F. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3530794 · Cited by 94 · see citing works
“In particular, scBERT demonstrates superiority in robustness and its ability to detect novel cell types. scGPT [76] is a single-cell multi-omics language model based on generative pre-training.”
CellFM: a large-scale foundation model pre-trained on transcriptomics of 100 million human cells
Zeng, Y., Xie, J., Shangguan, N., Wei, Z., Li, W., Su, Y., Yang, S., Zhang, C., Zhang, J., Fang, N., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59926-5 · Cited by 93 · see citing works
Democratizing protein language models with parameter-efficient fine-tuning
Sledzieski, S., Kshirsagar, M., Baek, M., Dodhia, R., Lavista Ferres, J., & Berger, B. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2405840121 · Cited by 86 · see citing works
Predicting cellular responses to perturbation across diverse contexts with State
Adduri, A. K., Gautam, D., Bevilacqua, B., Naghipourfar, M., Imran, A., Shah, R., Teyssier, N., Verma, R., Carpenter, C., Eraslan, B., et al. (2026) · Cell · doi:10.1016/j.cell.2026.07.052 · Cited by 100 · see citing works
Nicheformer: a foundation model for single-cell and spatial omics
Tejada-Lapuerta, A., Schaar, A. C., Gutgesell, R., Palla, G., Halle, L., Minaeva, M., Vornholz, L., Dony, L., Drummer, F., Richter, T., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02814-z · Cited by 100 · see citing works
Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective
Ge, S., Sun, S., Xu, H., Cheng, Q. S., & Ren, Z. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf136 · Cited by 89 · see citing works
Epigenomic heterogeneity as a source of tumour evolution
Laisné, M., Lupien, M., & Vallot, C. (2024) · Nature reviews. Cancer · doi:10.1038/s41568-024-00757-9 · Cited by 89 · see citing works
The future of rapid and automated single-cell data analysis using reference mapping
Lotfollahi, M., Hao, Y., Theis, F. J., & Satija, R. (2024) · Cell · doi:10.1016/j.cell.2024.03.009 · Cited by 77 · see citing works
Benchmarking algorithms for single-cell multi-omics prediction and integration
Hu, Y., Wan, S., Luo, Y., Li, Y., Wu, T., Deng, W., Jiang, C., Jiang, S., Zhang, Y., Liu, N., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02429-w · Cited by 75 · see citing works
Language models for biological research: a primer
Simon, E. P., Swanson, K., & Zou, J. (2024) · Nature Methods · doi:10.1038/s41592-024-02354-y · Cited by 76 · see citing works
Hallmarks of artificial intelligence contributions to precision oncology
Chang, T., Park, S., Schäffer, A. A., Jiang, P., & Ruppin, E. (2025) · Nature Cancer · doi:10.1038/s43018-025-00917-2 · Cited by 80 · see citing works
Digital twins in healthcare: a comprehensive review and future directions
Rudsari, H. K., Tseng, B., Zhu, H., Song, L., Gu, C., Roy, A., Irajizad, E., Butner, J., Long, J., & Do, K.-A. (2025) · Frontiers Digit. Health · doi:10.3389/fdgth.2025.1633539 · Cited by 104 · see citing works
Database resources of the National Genomics Data Center, China National Center for Bioinformation in 2026
Partners, C. M. A., Bào, Y., Zhang, Z., Zhao, W., Xiao, J., Song, S., He, S., Zhang, G., Li, Y., Zhao, G., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1172 · Cited by 96 · see citing works
Optimal transport for single-cell and spatial omics
Bunne, C., Schiebinger, G., Krause, A., Regev, A., & Cuturi, M. (2024) · Nature Reviews Methods Primers · doi:10.1038/s43586-024-00334-2 · Cited by 71 · see citing works
Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75 · see citing works
Deep learning-based predictions of gene perturbation effects do not yet outperform simple linear baselines
Ahlmann-Eltze, C., Huber, W., & Anders, S. (2024) · doi:10.1101/2024.09.16.613342 · Cited by 268 · see citing works
Advances and applications in single-cell and spatial genomics
Wang, J., Ye, F., Chai, H., Jiang, Y., Wang, T., Ran, X., Xia, Q., Xu, Z., Fu, Y., Zhang, G., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-024-2770-x · Cited by 69 · see citing works
Nicheformer: A Foundation Model for Single-Cell and Spatial OmicsPreprint
Schaar, A. C., Tejada-Lapuerta, A., Palla, G., Gutgesell, R. M., Halle, L., Minaeva, M., Vornholz, L., Dony, L., Drummer, F., Bahrami, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.15.589472 · Cited by 66 · see citing works
Harnessing artificial intelligence to fill global shortfalls in biodiversity knowledge
Pollock, L. J., Kitzes, J. A., Beery, S., Gaynor, K. M., Jarzyna, M. A., Aodha, O. M., Meyer, B. E., Rolnick, D., Taylor, G. W., Tuia, D., et al. (2025) · Nature Reviews Biodiversity · doi:10.1038/s44358-025-00022-3 · Cited by 74 · see citing works
Considerations for building and using integrated single-cell atlases
Hrovatin, K., Sikkema, L., Shitov, V. A., Heimberg, G., Shulman, M., Oliver, A. J., Mueller, M. F., Ibarra, I. L., Wang, H., Ramírez-Suástegui, C., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02532-y · Cited by 67 · see citing works
Integrated single-cell atlas of human atherosclerotic plaques
Traeuble, K., Munz, M., Pauli, J., Sachs, N., Vafadarnejad, E., Carrillo‐Roa, T., Mäegdefessel, L., Kastner, P., & Heinig, M. (2025) · Nature Communications · doi:10.1038/s41467-025-63202-x · Cited by 72 · see citing works
Cell2Sentence: Teaching Large Language Models the Language of BiologyPreprint
Levine, D. M., Rizvi, S. A., Lévy, S., Pallikkavaliyaveetil, N., Zhang, D., Chen, X., Ghadermarzi, S., Wu, R., Zheng, Z., Vrkic, I., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.11.557287 · Cited by 64 · see citing works
“This formatting enables LLMs to process and interpret this information (see Figures 1 and 2) while also maintaining the richness and complexity in single-cell data (Figures 3 and 4).”
MethodInfluential
Enablers and challenges of spatial omics, a melting pot of technologies
Alexandrov, T., Sáez-Rodríguez, J., & Saka, S. K. (2023) · Molecular Systems Biology · doi:10.15252/msb.202110571 · Cited by 64 · see citing works
Evaluating the Utilities of Foundation Models in Single-cell Data Analysis
Liu, T., Li, K., Wang, Y., Li, H., & Zhao, H. (2023) · doi:10.1101/2023.09.08.555192 · Cited by 62 · see citing works
Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system
Schäfer, P. S. L., Dimitrov, D., Villablanca, E. J., & Sáez-Rodríguez, J. (2024) · Nature Immunology · doi:10.1038/s41590-024-01768-2 · Cited by 66 · see citing works
“Deep learning models that are trained on millions of scRNA-seq profiles can learn intricate relationships between genes, surpassing the capabilities of classical coexpression analysis 104,105 .”
Evaluating the Utilities of Foundation Models in Single‐Cell Data Analysis
Liu, T., Li, K., Wang, Y., Li, H., & Zhao, H. (2026) · Advanced Science · doi:10.1002/advs.202514490 · Cited by 76 · see citing works
Assessing the limits of zero-shot foundation models in single-cell biology
Kedzierska, K. Z., Crawford, L., Amini, A. P., & Lu, A. X. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.16.561085 · Cited by 61 · see citing works
“In this study, we assessed the zero-shot performance of two proposed foundation models in single-cell biology: Geneformer [5] and scGPT [6] - representative examples in a rapidly evolving field.”
BackgroundMethodInfluential
Progress and opportunities of foundation models in bioinformatics
Li, Q., Hu, Z., Wang, Y., Li, L., Fan, Y., King, I., Jia, G., Wang, S., Song, L., & Li, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae548 · Cited by 62 · see citing works
“Therefore, customized fine-tuning can correct sequencing batches from multiple datasets [42].”
BackgroundMethodInfluential
Foundation models and intelligent decision-making: Progress, challenges, and perspectives
Huang, J., Xu, Y., Wang, Q., Wang, Q., Liang, X., Wang, F., Zhang, Z., Wei, W., Zhang, B., Huang, L., et al. (2025) · The Innovation · doi:10.1016/j.xinn.2025.100948 · Cited by 76 · see citing works
Perturb-Multimodal: A platform for pooled genetic screens with imaging and sequencing in intact mammalian tissue
Saunders, R. A., Allen, W. E., Pan, X., Sandhu, J., Lu, J., Lau, T. K., Smolyar, K., Sullivan, Z. A., Dulac, C. G., Weissman, J. S., et al. (2025) · Cell · doi:10.1016/j.cell.2025.05.022 · Cited by 76 · see citing works
A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model
Xu, Y., Wang, Y., Zhou, F., Ma, J., Jin, C., Yang, S., Li, J., Zhang, Z., Zhao, C., Zhou, H., et al. (2024) · arXiv · doi:10.48550/arxiv.2407.15362 · Cited by 60 · see citing works
scTab: Scaling cross-tissue single-cell annotation models
Fischer, F., Fischer, D. S., Mukhin, R. S., Isaev, A. V., Biederstedt, E., Villani, A., & Theis, F. J. (2024) · Nature Communications · doi:10.1038/s41467-024-51059-5 · Cited by 67 · see citing works
A multimodal knowledge-enhanced whole-slide pathology foundation model
Xu, Y., Wang, Y., Zhou, F., Ma, J., Jin, C., Yang, S., Li, J., Zhang, Z., Zhao, C., Zhou, H., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-66220-x · Cited by 74 · see citing works
Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models
Yue, T., Wang, Y., Zhang, L., Gu, C., Xue, H., Wang, W., Lyu, Q., & Dun, Y. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242115858 · Cited by 166 · see citing works
Artificial General Intelligence for Medical Imaging Analysis
Li, X., Zhao, L., Zhang, L., Wu, Z., Liu, Z., Jiang, H., Cao, C., Xu, S., Li, Y., Dai, H., et al. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3493775 · Cited by 68 · see citing works
Evaluating generalizability of artificial intelligence models for molecular datasets
Ektefaie, Y., Shen, A., Bykova, D., Marin, M. G., Zitnik, M., & Farhat, M. (2024) · bioRxiv · doi:10.1038/s42256-024-00931-6 · Cited by 75 · see citing works
“https://doi.org/10.1038/s42256-024-00931-6”
Influential
Large language models in plant biology
Lam, H. Y. I., Ong, X. E., & Mutwil, M. (2024) · Trends in Plant Science · doi:10.1016/j.tplants.2024.04.013 · Cited by 59 · see citing works
Simple and effective embedding model for single-cell biology built from ChatGPT
Chen, Y. T., & Zou, J. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01284-6 · Cited by 172 · see citing works
“The patterns of average gene expressions for identified gene programmes in different cell types are congruent with those previously identified in Cui et al. 3 .”
Influential
Data science opportunities of large language models for neuroscience and biomedicine
Bzdok, D., Thieme, A., Levkovskyy, O., Wren, P. B., Ray, T. S., & Reddy, S. (2024) · Neuron · doi:10.1016/j.neuron.2024.01.016 · Cited by 58 · see citing works
TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology
Wang, F.-A., Zhuang, Z., Gao, F., He, R., Zhang, S., Wang, L., Liu, J., & Li, Y. (2024) · Genome Biology · doi:10.1186/s13059-024-03293-9 · Cited by 51 · see citing works
A mini-review on perturbation modelling across single-cell omic modalities
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A machine learning approach to leveraging electronic health records for enhanced omics analysis
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Authors: Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., & Wang, B.
Affiliations: University Health Network (CA)
University of Toronto (CA)
Corresponding author: Wang, Bo
ORCID: Cui, Haotian
Maan, Hassaan
Pang, Kuan
Wang, Bo
Venue: Nature Methods
Publisher: openRxiv
Published: 2023-05-01
Volume: 21
Issue: 8
Pages: 1470-1480
ISSN: 1548-7091 (ISSN-L)
DOI: 10.1101/2023.04.30.538439
OpenAlex: W4392168151
Cited by: 1,383
References: 98
Citations by year: 2026: 581 · 2025: 520 · 2024: 248 · 2023: 31 · 2021: 1 · 2014: 1
FWCI: 158.42
Type: journal article
Language: en
Open access: green
doi.org
Licence: cc-by-nc-nd
Locations: Nature Methods (publishedVersion)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
Funders: University Health Network
Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (DGECR-2020-00294, RGPIN-2020-06189)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords: scGPT, single-cell multi-omics, foundation models, generative pre-trained transformer, single-cell RNA sequencing, cell type annotation, multi-omics integration, perturbation response prediction, gene network inference, transfer learning, single-cell biology
MeSH terms: Multiomics, Algorithms, Artificial Intelligence, Humans, Computational Biology, Gene Regulatory Networks, Single-Cell Analysis

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scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI

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