Stefano Cacciatore, Claudio Luchinat, Leonardo Tenori
Knowledge discovery by accuracy maximization
KODAMA (KnOwledge Discovery by Accuracy MAximization) is an unsupervised and semisupervised learning algorithm that performs feature extraction from noisy and high-dimensional data.

Stefano Cacciatore, Leonardo Tenori, Claudio Luchinat, Phillip R. Bennett, and David A. MacIntyre
Knowledge Discovery by Accuracy Maximization
An unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data.

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.

c(person("Stefano", "Cacciatore", role = c("aut", "trl", "cre"),email = "tkcaccia@gmail.com", comment = c(ORCID = "0000-0001-7052-7156")),
person("Leonardo", "Tenori", role = "aut",email = "tenori@cerm.unifi.it", comment = c(ORCID = "0000-0001-6438-059X")))
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. Based on Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA.
(2017) Bioinformatics <doi:10.1093/bioinformatics/btw705> and Cacciatore S, Luchinat C, Tenori L. (2014)
Proc Natl Acad Sci USA <doi:10.1073/pnas.1220873111>.
Stefano Cacciatore [aut, trl, cre]
(<https://orcid.org/0000-0001-7052-7156>),
Leonardo Tenori [aut] (<https://orcid.org/0000-0001-6438-059X>)