Reliability of predicting absolute affinities of strong binding MHC Class I and II epitopes for (a) NetMHC4, (b) mhcflurry-class I, (c) nn_align, and (d) mhcflurry-class II. Measurement and prediction values were represented as 1-log10(IC50)/log10(50000 nM) and were light-colored based on 2-D data density. Grey dotted lines mark 50 nM threshold (y = 0.638) and grey dashed lines mark 500 nM (y = 0.426) threshold. Red lines show the linear regression of the data. FNr(50 nM) indicates the false negative rate of classifying strong binders.</p
Motivation: Numerous in silico methods predicting peptide binding to major histocompatibility comple...
<p>Number of predicted T-cell epitopes from the 38-mer C-terminal fragment of α-Syn are in brackets....
BACKGROUND: It is important to accurately determine the performance of peptide:MHC binding predictio...
Multiclass classification performance of MHC class I binding epitope prediction tools: (a) VUS; (b) ...
Multiclass classification performance of MHC-II binding epitope prediction tools: (a) VUS; (b) SPE; ...
(a) Box plots showing the quartile distribution of binding affinity rankings as predicted by NetMHC4...
A number of machine learning-based predictors have been developed for identifying immunogenic T-cell...
Motivation: Computational methods for the prediction of peptide-MHC binding have become an integral ...
<p>For each MHC allele, the number of predicted epitopes within a given synthetic peptide is shown o...
Background: Protein antigens and their specific epitopes are formulation targets for epitope-based v...
Abstract Background Experimental screening of large sets of peptides with respect to their MHC bindi...
Motivation: Numerous in silico methods predicting peptide binding to major histocompatibility comple...
BACKGROUND: Protein antigens and their specific epitopes are formulation targets for epitope-based v...
<b>Background</b> Protein antigens and their specific epitopes are formulation target...
MHC-peptide binding prediction has been widely used for understanding the immune response of individ...
Motivation: Numerous in silico methods predicting peptide binding to major histocompatibility comple...
<p>Number of predicted T-cell epitopes from the 38-mer C-terminal fragment of α-Syn are in brackets....
BACKGROUND: It is important to accurately determine the performance of peptide:MHC binding predictio...
Multiclass classification performance of MHC class I binding epitope prediction tools: (a) VUS; (b) ...
Multiclass classification performance of MHC-II binding epitope prediction tools: (a) VUS; (b) SPE; ...
(a) Box plots showing the quartile distribution of binding affinity rankings as predicted by NetMHC4...
A number of machine learning-based predictors have been developed for identifying immunogenic T-cell...
Motivation: Computational methods for the prediction of peptide-MHC binding have become an integral ...
<p>For each MHC allele, the number of predicted epitopes within a given synthetic peptide is shown o...
Background: Protein antigens and their specific epitopes are formulation targets for epitope-based v...
Abstract Background Experimental screening of large sets of peptides with respect to their MHC bindi...
Motivation: Numerous in silico methods predicting peptide binding to major histocompatibility comple...
BACKGROUND: Protein antigens and their specific epitopes are formulation targets for epitope-based v...
<b>Background</b> Protein antigens and their specific epitopes are formulation target...
MHC-peptide binding prediction has been widely used for understanding the immune response of individ...
Motivation: Numerous in silico methods predicting peptide binding to major histocompatibility comple...
<p>Number of predicted T-cell epitopes from the 38-mer C-terminal fragment of α-Syn are in brackets....
BACKGROUND: It is important to accurately determine the performance of peptide:MHC binding predictio...