Blog
Longer reads from the cancer3.ai team — explained carefully, illustrated, and sourced.
Thirty-four letters of the lock: how antigen-presentation models actually work
Our previous article gave antigen-presentation models five paragraphs. Here we take them apart properly — and from scratch, assuming no biology. What an allele is and why the same cell looks different to two people, where the idea of describing the HLA groove with thirty-four letters comes from, how the mass-spectrometry data is physically produced, why the model invents its own missing labels, and what the numbers these tools advertise actually measure.
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The algorithms behind the vaccine: which machine-learning models pick a tumour's 34 targets
An individualized mRNA vaccine targets 34 neoantigens chosen from thousands of mutations. That choice is not made by a human — it is made by neural networks. We take the pipeline apart step by step: variant calling, pan-specific antigen-presentation models, deep learning on mass spectrometry — and we say plainly where public knowledge ends and proprietary code begins.
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mRNA cancer vaccines: teaching the immune system to recognise a tumour
The first positive phase 3 result in history for an mRNA cancer therapy. We explain, step by step and with illustrations, how an individualized vaccine teaches the immune system to recognise one patient's tumour.
Read the article →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.