Selected article for: "dataset validation and HLA II dataset ViPR validation"
Author: Asaf Poran; Dewi Harjanto; Matthew Malloy; Michael S. Rooney; Lakshmi Srinivasan; Richard B. Gaynor
Title: Sequence-based prediction of vaccine targets for inducing T cell responses to SARS-CoV-2 utilizing the bioinformatics predictor RECON
  • Document date: 2020_4_8
    • ID: 54mx8v4i_4
    Snippet: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.06.027805 doi: bioRxiv preprint 7 1 Binding prediction for ViPR Coronaviridae family T cell epitopes 2 Peptide-HLA-I allele pairs in the ViPR validation dataset were scored using RECON's HLA-I 3 binding predictor , a neural network-based model trained on mass spectrometry data (26) . 4 Similarly, peptide-HLA-II allele pairs in the ViPR va.....
    Document: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.06.027805 doi: bioRxiv preprint 7 1 Binding prediction for ViPR Coronaviridae family T cell epitopes 2 Peptide-HLA-I allele pairs in the ViPR validation dataset were scored using RECON's HLA-I 3 binding predictor , a neural network-based model trained on mass spectrometry data (26) . 4 Similarly, peptide-HLA-II allele pairs in the ViPR validation dataset were scored using 5 RECON's HLA-II binding predictor, a recently published convolutional neural network-based 6 model trained on mono-allelic mass spectrometry data (27). When applying the HLA-II binding 7 predictor, we used the highest score for all 12-20mers within a given assay peptide. This is meant 8 to account for the fact that the predictor is trained on ligands observed via mass spectrometry and 9 may learn processing rules that are irrelevant for assays that do not incorporate processing and 10 presentation.

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