School of Pharmaceutical Science and Technology, Tianjin University , 92 Weijin Road, Tianjin 300072, China.
School of Biological Sciences, Nanyang Technological University , 60 Nanyang Drive, Singapore 637551.
J Proteome Res. 2017 Aug 4;16(8):3102-3112. doi: 10.1021/acs.jproteome.7b00363. Epub 2017 Jul 7.
Protein complex-based feature selection (PCBFS) provides unparalleled reproducibility with high phenotypic relevance on proteomics data. Currently, there are five PCBFS paradigms, but not all representative methods have been implemented or made readily available. To allow general users to take advantage of these methods, we developed the R-package NetProt, which provides implementations of representative feature-selection methods. NetProt also provides methods for generating simulated differential data and generating pseudocomplexes for complex-based performance benchmarking. The NetProt open source R package is available for download from https://github.com/gohwils/NetProt/releases/ , and online documentation is available at http://rpubs.com/gohwils/204259 .
基于蛋白质复合物的特征选择（PCBFS）在蛋白质组学数据方面提供了无与伦比的可重复性和高度的表型相关性。目前有五个 PCBFS 范例，但并非所有代表性方法都已实现或可供随时使用。为了让普通用户能够利用这些方法，我们开发了 R 包 NetProt，它提供了代表性特征选择方法的实现。NetProt 还提供了生成模拟差异数据和生成基于复合物的性能基准测试伪复合物的方法。NetProt 的开源 R 包可从 https://github.com/gohwils/NetProt/releases/ 下载，在线文档可在 http://rpubs.com/gohwils/204259 查看。
