kfold.Entropy: Leave-one-out cross-training for Renyi entropy (ITEME)

In MEET: MEET: Motif Elements Estimation Toolkit

Description

Given a training sequence set, the optimal value for Renyi entropy has been estimated by means of leave-one-out cross-training from q-value set. For each q-value, the ROC curve has been calculated. From this results, the optimal q-value has been considered according to the area under convex surface maximum.

Usage

kfold.Entropy(iicc, TF)

Arguments

iicc	A set of inicial conditions for the MEET-package (mode, method, background, alignment, threshold, parameters, Transcriptionfactor, nummotif, lenmotif, sentit, position, missing, vector, gapopen, maxiters, gapextend)
TF	A set of nucleotide sequences

Details

This function integrates the Shannon entropy for Renyi Order equal 1. Moreover, it contains a set of function for the detection of transcription factor binding sites:correction.entropy.R, correction.redundancy.R, entropy.R entropy.max.R, entropy.corrected.R, probability.R, CalculRedundancy.R, diff.ructions.R, redundancy.R, missing.fun.R, ROC.R, detector_1rOrdre_diff.R, pvalue.R.

Author(s)

Joan Maynou <[e-mail]>

References

J. Maynou, J.-J. Gallardo-Chacon, M. Vallverdu, P. Caminal, and A. Perera, Computational detection of transcription factor binding sites through differential renyi entropy, Information Theory, IEEE Transactions on, vol. 56, no. 2, pp. 734, feb. 2010.

See Also

kfold.Divergence, kfold.MEME, kfold.MDscan, kfold.MATCH and kfold.PCA

Examples

 
data(iicc)
pathMEET<-system.file("exdata",package="MEET")
kfold.Entropy(iicc,TF=paste(pathMEET,"AP1.fa",sep="/"))

MEET documentation built on May 2, 2019, 5:52 p.m.