SpaCEM(3): a software for biological module detection when data is incomplete, high dimensional and dependent

Summary: Among classical methods for module detection, SpaCEM(3) provides ad hoc algorithms that were shown to be particularly well adapted to specific features of biological data: high-dimensionality, interactions between components (genes) and integrated treatment of missingness in observations. T...

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Detalles Bibliográficos
Autores principales: Vignes, Matthieu, Blanchet, Juliette, Leroux, Damien, Forbes, Florence
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2011
Materias:
Applications Note
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3051335/
https://www.ncbi.nlm.nih.gov/pubmed/21296754
http://dx.doi.org/10.1093/bioinformatics/btr034
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author Vignes, Matthieu
Blanchet, Juliette
Leroux, Damien
Forbes, Florence
author_facet Vignes, Matthieu
Blanchet, Juliette
Leroux, Damien
Forbes, Florence
author_sort Vignes, Matthieu
collection PubMed
description Summary: Among classical methods for module detection, SpaCEM(3) provides ad hoc algorithms that were shown to be particularly well adapted to specific features of biological data: high-dimensionality, interactions between components (genes) and integrated treatment of missingness in observations. The software, currently in its version 2.0, is developed in C++ and can be used either via command line or with the GUI under Linux and Windows environments. Availability: The SpaCEM(3) software, a documentation and datasets are available from http://spacem3.gforge.inria.fr/. Contact: matthieu.vignes@toulouse.inra.fr; SpaCEM3-help@lists.gforge.inria.fr
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spelling pubmed-30513352011-03-10 SpaCEM(3): a software for biological module detection when data is incomplete, high dimensional and dependent Vignes, Matthieu Blanchet, Juliette Leroux, Damien Forbes, Florence Bioinformatics Applications Note Summary: Among classical methods for module detection, SpaCEM(3) provides ad hoc algorithms that were shown to be particularly well adapted to specific features of biological data: high-dimensionality, interactions between components (genes) and integrated treatment of missingness in observations. The software, currently in its version 2.0, is developed in C++ and can be used either via command line or with the GUI under Linux and Windows environments. Availability: The SpaCEM(3) software, a documentation and datasets are available from http://spacem3.gforge.inria.fr/. Contact: matthieu.vignes@toulouse.inra.fr; SpaCEM3-help@lists.gforge.inria.fr Oxford University Press 2011-03-15 2011-02-03 /pmc/articles/PMC3051335/ /pubmed/21296754 http://dx.doi.org/10.1093/bioinformatics/btr034 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Vignes, Matthieu
Blanchet, Juliette
Leroux, Damien
Forbes, Florence
SpaCEM(3): a software for biological module detection when data is incomplete, high dimensional and dependent
title SpaCEM(3): a software for biological module detection when data is incomplete, high dimensional and dependent
title_full SpaCEM(3): a software for biological module detection when data is incomplete, high dimensional and dependent
title_fullStr SpaCEM(3): a software for biological module detection when data is incomplete, high dimensional and dependent
title_full_unstemmed SpaCEM(3): a software for biological module detection when data is incomplete, high dimensional and dependent
title_short SpaCEM(3): a software for biological module detection when data is incomplete, high dimensional and dependent
title_sort spacem(3): a software for biological module detection when data is incomplete, high dimensional and dependent
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3051335/
https://www.ncbi.nlm.nih.gov/pubmed/21296754
http://dx.doi.org/10.1093/bioinformatics/btr034
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