Ensemble building and statistical mechanics methods for MHC-peptide binding prediction
نویسندگان
چکیده
منابع مشابه
On Evaluating MHC-II Binding Peptide Prediction Methods
Choice of one method over another for MHC-II binding peptide prediction is typically based on published reports of their estimated performance on standard benchmark datasets. We show that several standard benchmark datasets of unique peptides used in such studies contain a substantial number of peptides that share a high degree of sequence identity with one or more other peptide sequences in th...
متن کاملStructural bioinformatics High-order neural networks and kernel methods for peptide-MHC binding prediction
Motivation: Effective computational methods for peptide-protein binding prediction can greatly help clinical peptide vaccine search and design. However, previous computational methods fail to capture key nonlinear high-order dependencies between different amino acid positions. As a result, they often produce low-quality rankings of strong binding peptides. To solve this problem, we propose nonl...
متن کاملHigh-order neural networks and kernel methods for peptide-MHC binding prediction
MOTIVATION Effective computational methods for peptide-protein binding prediction can greatly help clinical peptide vaccine search and design. However, previous computational methods fail to capture key nonlinear high-order dependencies between different amino acid positions. As a result, they often produce low-quality rankings of strong binding peptides. To solve this problem, we propose nonli...
متن کاملPeptide length-based prediction of peptide-MHC class II binding
MOTIVATION Algorithms for predicting peptide-MHC class II binding are typically similar, if not identical, to methods for predicting peptide-MHC class I binding despite known differences between the two scenarios. We investigate whether representing one of these differences, the greater range of peptide lengths binding MHC class II, improves the performance of these algorithms. RESULTS A non-...
متن کاملMHCPred: a server for quantitative prediction of peptide-MHC binding
Accurate T-cell epitope prediction is a principal objective of computational vaccinology. As a service to the immunology and vaccinology communities at large, we have implemented, as a server on the World Wide Web, a partial least squares-based multivariate statistical approach to the quantitative prediction of peptide binding to major histocom- patibility complexes (MHC), the key checkpoint on...
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ژورنال
عنوان ژورنال: Computer Research and Modeling
سال: 2020
ISSN: 2076-7633,2077-6853
DOI: 10.20537/2076-7633-2020-12-6-1383-1395