AI in Oncology · AI Models

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.

Open-source pan-allele presentation predictor whose distinctive idea is a separate antigen-processing model: it reads the peptide together with fifteen amino acids of flanking sequence on each side, because the proteasome cut depends on what lies around the cut site.

Allele-specific LSTM networks — one per allele, 148 for class I — that read peptides letter by letter, so no alignment or padding is needed, and that are trained by transfer learning from the data-richest allele.

Position-weight-matrix predictor built on the observation that class I ligands show almost no dependence between positions; version 3.0 closes the circle by having a neural network predict the matrix from the same 34 groove residues.

The class II counterpart of NetMHCpan, released in the same paper: predicts presentation by HLA-DR, -DQ and -DP, whose groove is open at both ends, so peptides are longer and the binding core has to be found inside a longer sequence.

The reference pan-allele predictor of MHC class I antigen presentation: one small neural network covers more than 11,000 MHC molecules because the groove itself is part of the input — a 34-residue pseudosequence next to the peptide.