kfold.MDscan: Leave-one-out cross-training for MDscan.

In MEET: MEET: Motif Elements Estimation Toolkit

Description

Given a training sequence set, the optimal length and number motif has been estimated by means of leave-one-out cross-training from length and number motif set. From each value, the ROC curve has been calculated. From this results, the optimal value has been considered according to the area under conver surface maximum

Usage

kfold.MDscan(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

Author(s)

Erola Pairo <[e-mail]> and Joan Maynou <[e-mail]>

References

X. S. Liu, D. L. Brutlag, and J. S. Liu, An algorithm for finding proteindna binding sites with applications to chromatin-immunopre- cipitation microarray experiments, Nat. Biotechnol. vol. 20, no. 8, pp. 835, Aug. 2002 [Online]. Available: http://dx.doi.org/10.1038/ nbt717

See Also

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

Examples

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

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