Article

Pep2Vec: An Interpretable Model for Peptide-MHC Presentation Prediction and Contaminant Identification in Ligandome Datasets

2024-10-17

Abstract excerpt

As personalized cancer vaccines advance, precise modeling of antigen presentation by MHC class I and II is crucial. High-quality training data is essential for clinical models. Existing deep learning models focus on prediction performance but lack interpretability. We introduce Pep2Vec, a modular, transformer-based model trained on MHC I and II ligandome data, transforming input sequences into interpretable vector...

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Literature Corpus work: 5122afec-ce2d-5428-8c9f-3c65bcfd49e4
DOI: 10.1101/2024.10.14.618255

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