CNN-PepPred: An open-source tool to create convolutional NN models for the discovery of patterns in peptide sets - Application to peptide-MHC class II binding prediction

CNN-PepPred: An open-source tool to create convolutional NN models for the discovery of patterns in peptide sets - Application to peptide-MHC class II binding prediction

Summary: The ability to unveil binding patterns in peptide sets has important applications in several biomedical areas, including the development of vaccines. We present an open-source tool, CNN-PepPred, that uses convolutional neural networks to discover such patterns, along with its application to...

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Bibliographic Details
Main Authors: Daura, X. (Author), Junet, V. (Author)
Format: Article
Language:English
Published: Oxford University Press 2021
Online Access:View Fulltext in Publisher
LEADER 01128nam a2200145Ia 4500
001 10.1093-bioinformatics-btab687
008 220427s2021 CNT 000 0 und d
020 |a 13674803 (ISSN) 
245 1 0 |a CNN-PepPred: An open-source tool to create convolutional NN models for the discovery of patterns in peptide sets - Application to peptide-MHC class II binding prediction 
260 0 |b Oxford University Press  |c 2021 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1093/bioinformatics/btab687 
520 3 |a Summary: The ability to unveil binding patterns in peptide sets has important applications in several biomedical areas, including the development of vaccines. We present an open-source tool, CNN-PepPred, that uses convolutional neural networks to discover such patterns, along with its application to peptide-HLA class II binding prediction. The tool can be used locally on different operating systems, with CPUs or GPUs, to train, evaluate, apply and visualize models. © 2021 The Author(s) 2021. Published by Oxford University Press. 
700 1 |a Daura, X.  |e author 
700 1 |a Junet, V.  |e author 
773 |t Bioinformatics 

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