# Foundation model

Source: https://onco.cc/terms/foundation-model/  
OnCo record `foundation-model` (Term). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

A foundation model is a large model trained on a vast amount of data without a specific task in mind, then adapted to many downstream uses.

## Summary

In artificial intelligence a foundation model is a machine-learning model trained on vast datasets so that it can be applied across a wide range of use cases; large language models are the common example (Wikipedia). In oncology the term covers pathology encoders trained on millions of tiles (UNI, Virchow), single-cell models trained on tens of millions of cells (UCE, GeneCompass), bulk transcriptome models (BulkFormer) and genomic sequence models (Evo 2). A model pretrained on single cells is out of distribution for bulk tumour data, so bulk-native and single-cell-native are different claims.

## Fields

- Kind: Term
- Last checked: 2026-09-24
- Also known as: foundation model; foundation models; FM; bulk-native foundation model; single-cell foundation model; large x model
- Tags: cansim-terms

## Notes

- Listed in the CanSim terms map 1.0.0 (docs/onco/terms.json, generated 2026-09-24), CC BY 4.0, attribution: CanSim project, an open, public-data-first cancer foundation-model programme; CanSim page path /terms/foundation-model.

## Sources

- Wikipedia: https://en.wikipedia.org/wiki/Foundation_model
- Wikipedia: https://en.wikipedia.org/wiki/Foundation_model

## Connected records

- ideas: [Patient-level multimodal foundation models for treatment selection](https://onco.cc/ideas/idea-multimodal-foundation-model/)
- terms: [Cancer AI vocabulary (CanSim terms map)](https://onco.cc/terms/cancer-ai-vocabulary/), [Fine-tuning versus frozen features, and LoRA](https://onco.cc/terms/fine-tuning-vs-frozen/), [GPU training and mixed precision](https://onco.cc/terms/mixed-precision-gpu/), [Pathology foundation models: UNI, UNI2, Virchow2, CTransPath, CONCH, TITAN](https://onco.cc/terms/pathology-foundation-models/), [Self-supervised pretraining (SSL)](https://onco.cc/terms/self-supervised-pretraining/), [Single-cell and transcriptome foundation models: UCE, GeneCompass, BulkFormer, BulkRNABert](https://onco.cc/terms/single-cell-foundation-models/), [Single-cell RNA sequencing (scRNA-seq, 10x Chromium)](https://onco.cc/terms/single-cell-rna-seq/), [Transformer and attention](https://onco.cc/terms/transformer-architecture/), [Virtual cell models and in-silico perturbation screens](https://onco.cc/terms/virtual-cell-models/)
- technologies: [AI compute and model platforms for oncology](https://onco.cc/technologies/ai-compute-platforms/), [Pathology & radiology foundation models](https://onco.cc/technologies/pathology-foundation-model/)

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JSON: https://onco.cc/api/v1/entities/foundation-model.json