KODAMA: an R package for knowledge discovery and data mining
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Published version
Supporting information
Author(s)
Cacciatore, S
Tenori, L
Luchinat, C
Bennett, P
MacIntyre, DA
Type
Journal Article
Abstract
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).
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).
Date Issued
2017-11-30
Date Acceptance
2016-11-04
Citation
Bioinformatics, 2017, 33 (4), pp.621-623
ISSN
1367-4803
Publisher
Oxford University Press (OUP)
Start Page
621
End Page
623
Journal / Book Title
Bioinformatics
Volume
33
Issue
4
Copyright Statement
© The Author 2016. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Sponsor
Medical Research Council (MRC)
Medical Research Council (MRC)
SPort Aiding medical Research for KidS (SPARKS)
Imperial College Healthcare NHS Trust- BRC Funding
Grant Number
MR/L009226/1
MR/L009226/1
13IMP01
RDF01 79560
Subjects
Science & Technology
Life Sciences & Biomedicine
Technology
Physical Sciences
Biochemical Research Methods
Biotechnology & Applied Microbiology
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Statistics & Probability
Biochemistry & Molecular Biology
Computer Science
Mathematics
CANCER
Bioinformatics
01 Mathematical Sciences
06 Biological Sciences
08 Information And Computing Sciences
Publication Status
Published