KODAMA: Knowledge Discovery by Accuracy Maximization
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>.
| Version: |
2.2 |
| Depends: |
R (≥ 2.10.0), stats, minerva, Rtsne |
| Imports: |
Rcpp (≥ 0.12.4) |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
rgl, knitr, rmarkdown |
| Published: |
2022-09-01 |
| Author: |
Stefano Cacciatore
 [aut, trl,
cre],
Leonardo Tenori
[aut] |
| Maintainer: |
Stefano Cacciatore <tkcaccia at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| CRAN checks: |
KODAMA results |
Documentation:
Downloads:
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