KODAMA algorithm is an unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data. It facilitates identification of patterns representing underlying groups on all samples in a data set. The algorithm was published by Cacciatore et al. 2014 <DOI:10.1073/pnas.1220873111>. Addition functions was introduced by Cacciatore et al. 2017 <DOI:10.1093/bioinformatics/btw705> to facilitate the identification of key features associated with the generated output and are easily interpretable for the user. Cross-validated techniques are also included in this package.
|Author||Stefano Cacciatore, Leonardo Tenori, Claudio Luchinat, Phillip R. Bennett, and David A. MacIntyre|
|Maintainer||Stefano Cacciatore <email@example.com>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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