netClass: 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) network smoothed t-statistic (stSVM).

Install the latest version of this package by entering the following in R:
AuthorYupeng Cun
Date of publication2013-12-03 22:44:46
MaintainerYupeng Cun <>
LicenseGPL (>= 2)

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Man pages

ad.matrix: An adjacency matrix of a sample graph...

calc.diffusionKernelp: Computing the Random Walk Kernel matrix of network

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

classify.frsvm: Training and predicting using FrSVM

classify.hubc: Training and predicting using hub nodes classification...

classify.pac: Training and predicting using PAC classification methods

classify.stsvm: Training and predicting using stSVM classification methods

cv.aep: Cross validation for aepSVM (aepSVM)

cv.frsvm: Cross validation for FrSVM

cv.hubc: Cross validation for hub nodes classification

cv.pac: Cross validation for Pathway Activities Classification(PAC)

cv.stsvm: Cross validation for smoothed t-statistic to select...

EN2SY: An list for mapping gene entre ids to symbol ids

expr: Two expression profile matrixs and their labels

getGeneRanking: Get gene ranking based on geneRank algorithm.

getGraphRank: Random walk kernel matrix smoothing t-statistic

Gs2: An subgraph of hub nodes

netClass-package: An R package for network-Based microarray Classification


pOfHubs: Computing p value of hubs using the permutation test

predictAep: Predicting the test tdata using aep trained model

predictFrsvm: Predicting the test data using frsvm trained model

predictHubc: Predicting the test data using hubc trained model

predictPac: Predicting the test data using pac trained model

predictStsvm: Predicting the test data using stsvm trained model

probeset2pathway: Generae a mean gene expression of genes of each pathway...

probeset2pathwayTrain: Search CROG in training data

probeset2pathwayTst: Applied CROG to testing data

train.aep: Training the data using aep methods

train.frsvm: Training the data using frsvm method

train.hubc: Predicting the data using hub nodes classification model

train.pac: Training the data using pac methods

train.stsvm: Training the data using stsvm methods


ad.matrix Man page
calc.diffusionKernelp Man page
classify.aep Man page
classify.frsvm Man page
classify.hubc Man page
classify.pac Man page
classify.stsvm Man page
cv.aep Man page
cv.frsvm Man page
cv.hubc Man page
cv.pac Man page
cv.stsvm Man page
EN2SY Man page
expr Man page
getGeneRanking Man page
getGraphRank Man page
Gs2 Man page
netClass Man page
netClass-package Man page
pGeneRANK Man page
pOfHubs Man page
predictAep Man page
predictFrsvm Man page
predictHubc Man page
predictPac Man page
predictStsvm Man page
probeset2pathway Man page
probeset2pathwayTrain Man page
probeset2pathwayTst Man page
train.aep Man page
train.frsvm Man page
train.hubc Man page
train.pac Man page
train.stsvm Man page

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