CNN-PepPred
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...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:303202 |
| Acceso en línea: | https://ddd.uab.cat/record/303202 https://dx.doi.org/urn:doi:10.1093/bioinformatics/btab687 |
| Access Level: | acceso abierto |
| Sumario: | 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. Availability and implementation: CNN-PepPred is freely available as a Python tool with a detailed User's Guide at https://github.com/ComputBiol-IBB/CNN-PepPred. The site includes the peptide sets used in this study, extracted from IEDB (www.iedb.org). Supplementary information: Supplementary data are available at Bioinformatics online. |
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