R/DWD: distance-weighted discrimination for classification, visualization and batch adjustment
R/DWD: distance-weighted discrimination for classification, visualization and batch adjustment
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DOI：
10.1093/bioinformatics/bts096
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发表时间：
2012-04-15
期刊：
影响因子：
5.8
通讯作者：
Marron, J. S.
中科院分区：
文献类型：
--
作者：
Huang, Hanwen;Lu, Xiaosun;Marron, J. S.
R/DWD是用于分类的可扩展包。它是基于最近开发的一种名为距离加权判别(DWD)的强大分类方法建立的。DWD与支持向量机有关，并且已被证明在生物信息学的基本情况下优于支持向量机，例如非常高维的数据。DWD已被证明在几个基本的生物信息学任务中非常有用，包括分类、数据可视化和消除偏差，如批处理效果。然而，早期的DWD实现依赖于不免费且需要许可的MatLab。R/DWD包的主要贡献是实现完全在R中，因此可以在没有任何许可或软件购买要求的情况下使用。此外，R/DWD还为二阶锥规划和二次规划提供了有效的求解器。
R/DWD is an extensible package for classification. It is built based on a recently developed powerful classification method called distance weighted discrimination (DWD). DWD is related to, and has been shown to be superior to, the support vector machine in situations that are fundamental to bioinformatics, such as very high dimensional data. DWD has proven to be very useful for several fundamental bioinformatics tasks, including classification, data visualization and removal of biases, such as batch effects. Earlier DWD implementations, however, relied on Matlab, which is not free and requires a license. The major contribution of the R/DWD package is an implementation that is completely in R and thus can be used without any requirements for licensing or software purchase. In addition, R/DWD also provides efficient solvers for second-order-cone-programming and quadratic programming.