train.aep: Training the data using aep methods

In netClass: netClass: An R Package for Network-Based Biomarker Discovery

Description

Training the data using aep methods

Usage

train.aep(x = x, y = y, DEBUG = FALSE, int = int, Gsub = Gsub, Cs = 10^(-3:3))

Arguments

x	expression data for training
y	a factor of length p comprising the class labels.
DEBUG	show debugging information in screen more or less.
int	Intersect of genes in network and gene expression profile.
Gsub	an adjacency matrix that represents the underlying biological network.
Cs	soft-margin tuning parameter of the SVM. Defaults to 10^c(-3:3).

Value

The returned lists

trained	The tranined models for traning folds
sig.genes	The differential expressed feature

Author(s)

Yupeng Cun [e-mail]

References

Guo et al., Towards precise classification of cancers based on robust gene functional expression profiles. BMC Bioinformatics 2005, 6:58.

See Also

See Also as cv.aep

Examples

#see cv.aep

netClass documentation built on May 29, 2017, 7:18 p.m.