Imperial College London
Alternate

DrStefanoCacciatore

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Honorary Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 2137s.cacciatore

 
 
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Location

 

Institute of Reproductive and Developmental BiologyHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Cacciatore:2017:bioinformatics/btw705,
author = {Cacciatore, S and Tenori, L and Luchinat, C and Bennett, P and MacIntyre, DA},
doi = {bioinformatics/btw705},
journal = {Bioinformatics},
pages = {621--623},
title = {KODAMA: an R package for knowledge discovery and data mining},
url = {http://dx.doi.org/10.1093/bioinformatics/btw705},
volume = {33},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Summary: KODAMA, a novel learning algorithm for unsuper-vised feature extraction, is specifically designed for analysing noisy and high-dimensional data sets. Here we present an R package of the algorithm with additional functions that allow improved interpretation of high-dimensional data. The pack-age requires no additional software and runs on all major plat-forms.Availability and Implementation: KODAMA is freely available from the R archive CRAN (http://cran.r-project.org). The soft-ware is distributed under the GNU General Public License (ver-sion 3 or later).
AU  - Cacciatore,S
AU  - Tenori,L
AU  - Luchinat,C
AU  - Bennett,P
AU  - MacIntyre,DA
DO  - bioinformatics/btw705
EP  - 623
PY  - 2017///
SN  - 1367-4803
SP  - 621
TI  - KODAMA: an R package for knowledge discovery and data mining
T2  - Bioinformatics
UR  - http://dx.doi.org/10.1093/bioinformatics/btw705
UR  - http://hdl.handle.net/10044/1/42338
VL  - 33
ER  -
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