AI in Oncology · AI Models
A researcher-grade catalog of AI models, datasets and open data needs in oncology — every card structured, sourced and dated.
Filter by task, data type, cancer, availability and regulatory status. Each card follows one model-card standard and links to Hugging Face, code, papers and the datasets it was trained or tested on. { } export JSON
DeepVariant
Deep-learning variant caller that turns aligned sequencing reads into pileup images and classifies genotypes with a CNN; widely used for germline calling, with the companion DeepSomatic extending the approach to tumour–normal somatic variants.
ESM-2 / ESMFold
Protein language models from 8 million to 15 billion parameters trained on UniRef sequences; embeddings power variant-effect and function prediction, and ESMFold predicts structure directly from a single sequence without MSAs.
AlphaMissense
Classifies the pathogenicity of every possible single amino-acid substitution in the human proteome — 71 million missense variants — by fine-tuning AlphaFold on population variant frequencies; a resource for interpreting variants of uncertain significance.
Cards follow the cancer3.ai model-card standard: AI_MODEL_CARD_STANDARD.md. Corrections and new entries: contact the editorial team; every fact needs a public source.
This page is educational — it is not medical advice and does not replace consultation with an oncologist. Diagnostic and treatment decisions are made solely by specialist physicians.