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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Oxford University Press
2021
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| Online Access: | View Fulltext in Publisher |
| Summary: | 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. |
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| ISBN: | 13674803 (ISSN) |
| DOI: | 10.1093/bioinformatics/btab687 |