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.
Datasets, registries and benchmarks you can train or evaluate oncology models on — with access conditions, licences, sizes, annotations and the models already using them. { } export JSON
CPTAC — Clinical Proteomic Tumor Analysis Consortium
Proteogenomic companion to TCGA: the largest public set where protein-level measurements, genomics and images exist for the same tumours.
DepMap — Cancer Dependency Map
Public map of cancer vulnerabilities in cell lines — the training and validation ground for target-discovery and drug-response models.
Genecorpus-30M
Genecorpus-30M is a pretraining corpus of about 30 million human single-cell transcriptomes assembled from 561 publicly available datasets, of which 27,406,217 cells passed quality filters. It was built to pretrain Geneformer (Theodoris et al., Nature 2023) and is released under Apache-2.0. Crucially for oncology use: cells with high mutational burden — malignant cells and immortalized cell lines — were deliberately excluded from the corpus.
TCGA — The Cancer Genome Atlas (via NCI Genomic Data Commons)
The reference multi-omics cancer cohort: molecular profiles, clinical outcomes and whole-slide images for 33 cancer types, downloadable through the GDC portal and API.
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.