ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Trustworthy Agentic AI in Bioinformatics: From Workflow Automation to Traceable and Validated Biological Inference.Biology · 2026Review
- A systematic assessment of single-cell language model configurations.NAR genomics and bioinformatics · 2026Article
- Evaluating the learnability of single-cell large language models on multiple tasks.BMC genomics · 2026Article
- SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration.Nature communications · 2026Article
- spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model.Genome research · 2026Article
- SpatialFusion: A lightweight multimodal foundation model for pathway-informed spatial niche mapping.bioRxiv : the preprint server for biology · 2026Article
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- Embeddings from language models are good learners for single-cell data analysis.Patterns (New York, N.Y.) · 2026Article
- Virtual Cells Need Context, Not Just Scale.bioRxiv : the preprint server for biology · 2026Article
- Prediction of outcome from spatial Protein profiling of triple-negative breast cancers.Communications medicine · 2026Article
- An expanded role for single-cell chemical genomics profiling in drug discovery.The Biochemical journal · 2026Review
- Stack: In-Context Learning of Single-Cell Biology.bioRxiv : the preprint server for biology · 2026Article
- A unified framework enables accessible deployment and comprehensive benchmarking of single-cell foundation models.bioRxiv : the preprint server for biology · 2026Article
- A perspective on developing foundation models for analyzing spatial transcriptomic data.Quantitative biology (Beijing, China) · 2025Article
- A comparison of computational methods for expression forecasting.Genome biology · 2025Article
- Heimdall: A Modular Framework for Tokenization in Single-Cell Foundation Models.bioRxiv : the preprint server for biology · 2025Article
- Transformative advances in single-cell omics: a comprehensive review of foundation models, multimodal integration and computational ecosystems.Journal of translational medicine · 2025Review
- Biology-driven insights into the power of single-cell foundation models.Genome biology · 2025Article
- CellNavi predicts genes directing cellular transitions by learning a gene graph-enhanced cell state manifold.Nature cell biology · 2025Article
- Single-cell foundation models: bringing artificial intelligence into cell biology.Experimental & molecular medicine · 2025Review
Corrections and comments
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5 authors.
Funding
Abstract
Foundation Models (FMs) have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of FMs for single-cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single-cell data. Overall, the top FMs include scGPT, Geneformer, and CellFM by considering model performances and user accessibility among ten single-cell FMs. However, by comparing these FMs with task-specific methods, we found that single-cell FMs may not consistently excel than task-specific methods in all tasks, which challenges the necessity of developing foundation models for single-cell analysis. In addition, we evaluated the effects of hyperparameters, initial settings, and stability for training single-cell FMs based on a proposed scEval framework, and provide guidelines for pre-training and fine-tuning to enhance the performances of single-cell FMs. Our work summarizes the current state of single-cell FMs, points to their constraints and avenues for future development, and offers a freely available evaluation pipeline to benchmark new models and improve method development.
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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.