netClass (version 1.2.1)

classify.aep: Training and predicting using aepSVM (aepSVM) classification methods

Description

Training and predicting using aepSVM (aepSVM) classification methods

Usage

classify.aep(fold, cuts, Cs, x, y, cv.repeat, int, DEBUG = DEBUG, Gsub)

Arguments

fold

number of -folds cross validation (CV)

cuts

list for randomly divide the training set in to x-x-folds CV

Cs

soft-margin tuning parameter of the SVM. Defaults to 10^c(-3:3).

x

gene expression data

y

class labels

cv.repeat

model for one CV training and predicting

int

Intersect of genes in network and gene expression profile.

DEBUG

show debugging information in screen more or less.

Gsub

an adjacency matrix that represents the underlying biological network.

Value

fold

the recored for test fold

auc

The AUC values of test fold

train

The tranined models for traning folds

feat

The feature selected by each by the train

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