Cacciatore Stefano, Tenori Leonardo, Luchinat Claudio, Bennett Phillip R, MacIntyre David A
Institute of Reproductive and Developmental Biology, Imperial College London, London, UK.
Department of Clinical and Experimental Medicine.
Bioinformatics. 2017 Feb 15;33(4):621-623. doi: 10.1093/bioinformatics/btw705.
KODAMA, a novel learning algorithm for unsupervised feature extraction, is specifically designed for analysing noisy and high-dimensional datasets. Here we present an R package of the algorithm with additional functions that allow improved interpretation of high-dimensional data. The package requires no additional software and runs on all major platforms.
KODAMA is freely available from the R archive CRAN ( http://cran.r-project.org ). The software is distributed under the GNU General Public License (version 3 or later).
Supplementary data are available at Bioinformatics online.
KODAMA是一种用于无监督特征提取的新型学习算法，专门设计用于分析噪声和高维数据集。在此，我们展示了该算法的R包，其附加功能可改进对高维数据的解释。该包无需额外软件，可在所有主流平台上运行。
KODAMA可从R存档库CRAN（http://cran.r-project.org）免费获取。该软件根据GNU通用公共许可证（第3版或更高版本）分发。
补充数据可在《生物信息学》在线获取。
