Thomas Lumley, based on code by Steve Oncley and Gordon Maclean

netClass: An R Package for Network-Based Microarray
Classification
netClass is an R packages for feature (gene) selection,
which employ the biological network information to microarray
classification. We implemented average gene expression of
pathway (aep), pathway activities classification (PAC), Hub
network Classification, filter via top ranked genes(FrSVM),
smoothed t-statistic(stSVM) for two classes microarray
classification which employed the prior information.
R (>= 2.12), AnnotationDbi, pathClass, Matrix,graph, igraph,
samr

netClass: An R Package for Network-Based Biomarker Discovery
netClass is an R package for network-based feature (gene)
selection for biomarkers discovery via integrating biological
information.  This package adapts the following 5 algorithms
for classifying and predicting gene expression data using prior
knowledge: 1) average gene expression of pathway (aep); 2)
pathway activities classification (PAC); 3) Hub network
Classification (hubc); 4) filter via top ranked genes (FrSVM);
5) smoothed t-statistic (stSVM).
R (>= 2.14), AnnotationDbi, pathClass, Matrix,graph, igraph,
samr

netClass is an R package for network-based feature (gene)
selection for biomarkers discovery via integrating biological
information.  This package adapts the following 5 algorithms
for classifying and predicting gene expression data using prior
knowledge: 1) average gene expression of pathway (aep); 2)
pathway activities classification (PAC); 3) Hub network
Classification (hubc); 4) filter via top ranked genes (FrSVM);
5) network smoothed t-statistic (stSVM).
AnnotationDbi, Matrix, ROCR, graph, igraph, samr

Shuchismita Sarkar [aut, cre],
Volodymyr Melnykov [aut]
Clustering unilayer and multilayer network data by means of finite mixtures is the main utility of 'netClust'.

Create interactive networked coincidences. It joins the data analysis power of R to study coincidences and the visualization libraries of JavaScript in one package.
Modesto Escobar <modesto@usal.es>,
Carlos Prieto <cprietos@usal.es>,
David Barrios <metal@usal.es>,
Luis Martinez-Uribe <lmartinez@usal.es>.