2003
DOI: 10.1034/j.1399-0039.2003.00112.x
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Sensitive quantitative predictions of peptide‐MHC binding by a ‘Query by Committee’ artificial neural network approach

Abstract: We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict binding vs non-binding peptides. Furthermore, quantitative ANN allowed a straightforward application of a 'Query by Committee' (QBC) principle whereby particularly information-rich peptides could be identified and subseque… Show more

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Cited by 308 publications
(213 citation statements)
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“…In contrast, neural networks (and other higher order data-mining methods) are ideally suited to capture and predict peptide-HLA-I binding including any correlated effects. However, the development of neural networks is dependent on the availability of a large body of data representing many different single peptide-binding events (31). To generate single peptide-binding data and support the development of neural networks, we used the PSCPL matrices to identify peptides that were predicted to bind to the different HLA-C allotypes.…”
Section: High-affinity Peptide Binders Predicted By Pscpl Matricesmentioning
confidence: 99%
“…In contrast, neural networks (and other higher order data-mining methods) are ideally suited to capture and predict peptide-HLA-I binding including any correlated effects. However, the development of neural networks is dependent on the availability of a large body of data representing many different single peptide-binding events (31). To generate single peptide-binding data and support the development of neural networks, we used the PSCPL matrices to identify peptides that were predicted to bind to the different HLA-C allotypes.…”
Section: High-affinity Peptide Binders Predicted By Pscpl Matricesmentioning
confidence: 99%
“…For the A1, A2, A3, A24, B7, B8, B27, B44, B58, and B62 supertype the affinity predictors are based on ANN [12,13]. This prediction method is available at http://cbs.dtu.dk/ services/NetMHC.…”
Section: Mhc Class I Affinity Predictionsmentioning
confidence: 99%
“…Accordingly, many attempts have been made to predict the outcome of the steps involved in antigen presentation. A number of methods have been developed that very reliably predict the binding affinity of peptides to the different MHC class I alleles [11][12][13][14]. Likewise, methods have been developed that predict the efficiency with which peptides of arbitrary length will be transported by TAP [15,16].…”
Section: Introductionmentioning
confidence: 99%
“…Previously proposed learning approaches for protein-peptide binding prediction, address the binding prediction problem using traditional margin based binary classifiers: for each protein a classifier is trained to distinguish binding peptides from non-binding peptides [6,4,16] (for a review see [9]). Recently, we proposed PepDist: a novel approach for predicting binding affinity based on learning peptide-peptide distance functions 1 [28].…”
Section: Learning Peptide-peptide Distance Functionsmentioning
confidence: 99%