CNN-PepPred：一个用于创建卷积神经网络模型的开源工具，用于发现肽集合中的模式-应用于肽-MHC 类 II 结合预测。
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.
机构信息
Anaxomics Biotech SL, Barcelona 08008, Spain.
Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Barcelona 08193, Spain.
出版信息
Bioinformatics. 2021 Dec 7;37(23):4567-4568. doi: 10.1093/bioinformatics/btab687.
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.
摘要
揭示肽集合中的结合模式在包括疫苗开发在内的几个生物医学领域有重要应用。我们介绍了一种开源工具 CNN-PepPred，它使用卷积神经网络来发现这些模式，并将其应用于肽-HLA 类 II 结合预测。该工具可以在不同操作系统上本地使用，支持 CPU 或 GPU，用于训练、评估、应用和可视化模型。
可用性和实现
CNN-PepPred 是一个免费的 Python 工具，详细的用户指南可在 https://github.com/ComputBiol-IBB/CNN-PepPred 上获取。该网站包括了从 IEDB（www.iedb.org）提取的用于本研究的肽集合。
补充信息
补充数据可在生物信息学在线获取。
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