iRafNet: Integrative Random Forest for Gene Regulatory Network Inference

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Provides a flexible integrative algorithm that allows information from prior data, such as protein protein interactions and gene knock-down, to be jointly considered for gene regulatory network inference.

Author
Francesca Petralia [aut, cre], Pei Wang [aut], Zhidong Tu [aut], Jialiang Yang [aut], Adele Cutler [ctb], Leo Breiman [ctb], Andy Liaw [ctb], Matthew Wiener [ctb]
Date of publication
2016-10-26 10:37:49
Maintainer
Francesca Petralia <francesca.petralia@mssm.edu>
License
GPL (>= 2)
Version
1.1-1
URLs

View on CRAN

Man pages

iRafNet
Integrative random forest for gene regulatory network...
iRafNet_network
Compute permutation-based FDR of importance scores and return...
iRafNet_permutation
Derive importance scores for one permuted data.
roc_curve
Plot receiver operating characteristic (ROC) curve for...
Run_permutation
Derive importance scores for M permuted data sets.

Files in this package

iRafNet
iRafNet/src
iRafNet/src/rf.h
iRafNet/src/regTree.c
iRafNet/src/regrf.c
iRafNet/src/rfutils.c
iRafNet/NAMESPACE
iRafNet/R
iRafNet/R/iRafNet_permutation.R
iRafNet/R/Run_permutation.R
iRafNet/R/IRafNet_network.R
iRafNet/R/iRafNet.R
iRafNet/R/roc_curve.R
iRafNet/MD5
iRafNet/DESCRIPTION
iRafNet/man
iRafNet/man/iRafNet_network.Rd
iRafNet/man/roc_curve.Rd
iRafNet/man/iRafNet_permutation.Rd
iRafNet/man/Run_permutation.Rd
iRafNet/man/iRafNet.Rd