AI in Oncology · AI Models

The largest real-world clinical sequencing registry in oncology — the place to test whether a variant-effect or biomarker model generalises beyond TCGA.

The reference affinity dataset behind fifteen years of binding predictors; its size and composition are why dataset composition, not architecture, drives reported performance.

The registry that trial-matching systems such as TrialGPT retrieve from; oncology is its largest therapeutic area.

Proteogenomic companion to TCGA: the largest public set where protein-level measurements, genomics and images exist for the same tumours.

Public map of cancer vulnerabilities in cell lines — the training and validation ground for target-discovery and drug-response models.

A benign-tissue reference peptidome: the set you check a candidate neoantigen against to make sure healthy tissue does not present it too.

The only prospective, third-party benchmark in the field. Its eight-year summary is sobering: leading methods are statistically indistinguishable, and a new method needs about four years before enough data accumulate to judge it.

The field's central repository of epitope data and the source of almost every training set for peptide–MHC models — and of their allele skew.

The naming authority for HLA alleles: the catalogue whose size — thirty thousand names against roughly a hundred well-measured alleles — defines the central problem of this field.

The exam-style benchmark on which Med-PaLM 2, GPT-4 and MedGemma report headline accuracy; useful for comparing models, not for judging clinical safety.

The reference collection of HLA binding motifs — and the clearest picture of the field's long tail: a million measured ligands still describe barely 135 of thirty thousand alleles.

The engineered-cell peptidome that gave the field clean allele labels; fifteen of its alleles had no described motif before, and the panel covers at least one allele in 95% of people worldwide.

The open archive of experimentally solved macromolecular structures, including oncology targets (kinases, KRAS, p53) and their drug complexes.

The literature corpus behind BiomedBERT, BiomedCLIP and CONCH's caption data; the entry point for any oncology NLP or vision-language pretraining.

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.

The main public archive of de-identified cancer imaging, organised into collections by disease and modality; the source of most public radiology training data in oncology.