Geneformer
Context-aware transformer for single-cell data enabling in silico deletion and network predictions; a Nature Protocols article describes target discovery with it. Zero-shot evaluation reports limitations. VC0009 VC0010 VC0050
Review pending Version 0.1.0 · status: draft · last expert review: not yet reviewed
Relations
| Subject | Relation | Object | Confidence | Evidence (role) |
|---|---|---|---|---|
| Geneformer | associated with | Single-cell foundation model | high | VC0009 supports From: AI virtual cell models |
| Geneformer | associated with | In silico perturbation | high | VC0009 supports VC0010 supports VC0038 · preprint supports From: AI virtual cell models · entity record |
| Geneformer | benchmarked on | Zero-shot evaluation | high | VC0050 benchmark Zero-shot limitations reported. |
Appears in research topics
- AI virtual cell models (Models)
Appears in library items (5)
Items whose title, tags or one-line summary match this entity (derived from the data), plus the items cited as evidence for the description.
- VC0038 · preprint ISP 3 Platform powered by Geneformer: Framework for Cross-Species, Sequential, and Multi-Gene In Silico Perturbation Screens with Application to iPS Cell State Transitions — Sanaki Y, Shigenobu S, Niwa R., bioRxiv, 2026
- VC0057 Assessing scale and predictive diversity in models for single-cell transcriptomics based on Geneformer. — Chen J, Schmidt F, Henao R., PLoS computational biology, 2026
- VC0010 Discovery of candidate therapeutic targets with Geneformer. — Zhang Y, Venkatesh MS, Theodoris CV., Nature protocols, 2026
- VC0050 Zero-shot evaluation reveals limitations of single-cell foundation models. — Kedzierska KZ, Crawford L, Amini AP, Lu AX., Genome biology, 2025
- VC0009 Transfer learning enables predictions in network biology. — Theodoris CV, Xiao L, Chopra A, Chaffin MD, Al Sayed ZR, Hill MC, Mantineo H, Brydon EM, Zeng Z, Liu XS, Ellinor PT., Nature, 2023
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