Package: geNetClassifier
Type: Package
Title: Classify diseases and build associated gene networks using gene
expression profiles
Version: 1.12.0
Date: 2015-05-05
Author: Sara Aibar, Celia Fontanillo and Javier De Las Rivas.
Bioinformatics and Functional Genomics Group. Cancer Research
Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain.
Maintainer: Sara Aibar <saibar@usal.es>
Depends: R (>= 2.10.1), Biobase (>= 2.5.5), EBarrays, minet, methods
Imports: e1071, graphics
Suggests: leukemiasEset, RUnit, BiocGenerics
Enhances: RColorBrewer, igraph, infotheo
Description: Comprehensive package to automatically train and validate a multi-class SVM classifier based on gene expression data. Provides transparent selection of gene markers, their coexpression networks, and an interface to query the classifier.
License: GPL (>= 2)
ZipData: no
URL: http://www.cicancer.org
LazyLoad: yes
Collate: class.GenesRanking.R class.GenesNetwork.R
class.GeneralizationError.R class.GeNetClassifierReturn.R
functions.private.R functions.public.R function.main.R
biocViews: Classification, DifferentialExpression, Microarray
NeedsCompilation: no
Packaged: 2016-05-04 04:54:56 UTC; biocbuild
Package: gaga
Version: 2.18.0
Date: 2015-06-14
Title: GaGa hierarchical model for high-throughput data analysis
Author: David Rossell <rosselldavid@gmail.com>.
Maintainer: David Rossell <rosselldavid@gmail.com>
Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv
Enhances: parallel
Description: Implements the GaGa model for high-throughput data
analysis, including differential expression analysis,
supervised gene clustering and classification. Additionally, it
performs sequential sample size calculations using the GaGa and
LNNGV models (the latter from EBarrays package).
License: GPL (>= 2)
biocViews: OneChannel, MassSpectrometry, MultipleComparison,
DifferentialExpression, Classification
NeedsCompilation: yes
Packaged: 2016-05-04 03:05:17 UTC; biocbuild
providing 
.