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...

Descripción completa

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
Descripción
Sumario: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

Ejemplares similares