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
Geneformer
Transformer pretrained on about 30 million single-cell transcriptomes (Genecorpus-30M) that learns gene-network context from ranked expression, enabling few-shot prediction of gene dosage effects, cell states and candidate therapeutic targets.
HLAthena
Presentation predictor trained on the mono-allelic peptidome of 95 cell lines — 186,464 peptides across 95 HLA alleles, fifteen of which had no described motif before — which is the dataset that changed this field more than any architectural idea.
scGPT
Generative pretrained transformer for single-cell multi-omics, trained on over 33 million cells, supporting cell-type annotation, batch integration, perturbation response prediction and gene-network inference.
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.