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Mfuzz: A software package for soft clustering of microarray data

For the analysis of microarray data, clustering techniques are frequently used. Most of such methods are based on hard clustering of data wherein one gene (or sample) is assigned to exactly one cluster. Hard clustering, however, suffers from several drawbacks such as sensitivity to noise and informa...

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Detalles Bibliográficos
Autores principales: Kumar, Lokesh, E. Futschik, Matthias
Formato: Texto
Lenguaje:English
Publicado: Biomedical Informatics Publishing Group 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2139991/
https://www.ncbi.nlm.nih.gov/pubmed/18084642
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author Kumar, Lokesh
E. Futschik, Matthias
author_facet Kumar, Lokesh
E. Futschik, Matthias
author_sort Kumar, Lokesh
collection PubMed
description For the analysis of microarray data, clustering techniques are frequently used. Most of such methods are based on hard clustering of data wherein one gene (or sample) is assigned to exactly one cluster. Hard clustering, however, suffers from several drawbacks such as sensitivity to noise and information loss. In contrast, soft clustering methods can assign a gene to several clusters. They can overcome shortcomings of conventional hard clustering techniques and offer further advantages. Thus, we constructed an R package termed Mfuzz implementing soft clustering tools for microarray data analysis. The additional package Mfuzzgui provides a convenient TclTk based graphical user interface. AVAILABILITY: The R package Mfuzz and Mfuzzgui are available at http://itb1.biologie.hu-berlin.de/~futschik/software/R/Mfuzz/index.html. Their distribution is subject to GPL version 2 license.
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spelling pubmed-21399912007-12-14 Mfuzz: A software package for soft clustering of microarray data Kumar, Lokesh E. Futschik, Matthias Bioinformation Software For the analysis of microarray data, clustering techniques are frequently used. Most of such methods are based on hard clustering of data wherein one gene (or sample) is assigned to exactly one cluster. Hard clustering, however, suffers from several drawbacks such as sensitivity to noise and information loss. In contrast, soft clustering methods can assign a gene to several clusters. They can overcome shortcomings of conventional hard clustering techniques and offer further advantages. Thus, we constructed an R package termed Mfuzz implementing soft clustering tools for microarray data analysis. The additional package Mfuzzgui provides a convenient TclTk based graphical user interface. AVAILABILITY: The R package Mfuzz and Mfuzzgui are available at http://itb1.biologie.hu-berlin.de/~futschik/software/R/Mfuzz/index.html. Their distribution is subject to GPL version 2 license. Biomedical Informatics Publishing Group 2007-05-20 /pmc/articles/PMC2139991/ /pubmed/18084642 Text en © 2007 Biomedical Informatics Publishing Group This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
spellingShingle Software
Kumar, Lokesh
E. Futschik, Matthias
Mfuzz: A software package for soft clustering of microarray data
title Mfuzz: A software package for soft clustering of microarray data
title_full Mfuzz: A software package for soft clustering of microarray data
title_fullStr Mfuzz: A software package for soft clustering of microarray data
title_full_unstemmed Mfuzz: A software package for soft clustering of microarray data
title_short Mfuzz: A software package for soft clustering of microarray data
title_sort mfuzz: a software package for soft clustering of microarray data
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2139991/
https://www.ncbi.nlm.nih.gov/pubmed/18084642
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