kfold.MEME: Leave-one-out cross-validation for MEME
In MEET: MEET: Motif Elements Estimation Toolkit
Description
Usage
Arguments
Details
Author(s)
References
See Also
Description
Given a training sequence set, the optimal length and number motif has been estimated by means of leave-one-out cross-validation 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
1
kfold.MEME(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 needs MEME/MAST software.
Author(s)
Joan Maynou <joan.maynouatupc.edu>
References
[5] T. Bailey and C. Elkan, Fitting a mixture model by expectation maximization to discover motifs in biopolymers, in Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology.AAAI Press, August 1994, pp. 28-36
See Also
kfold.PredictDivergence, kfold.PredictInformationTheory, MDscan, kfold.MATCH and kfold.PCA
MEET documentation built on May 2, 2019, 1:45 p.m.
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Description Usage Arguments Details Author(s) References See Also
Description
Given a training sequence set, the optimal length and number motif has been estimated by means of leave-one-out cross-validation 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
1 | kfold.MEME(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 needs MEME/MAST software.
Author(s)
Joan Maynou <joan.maynouatupc.edu>
References
[5] T. Bailey and C. Elkan, Fitting a mixture model by expectation maximization to discover motifs in biopolymers, in Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology.AAAI Press, August 1994, pp. 28-36
See Also
kfold.PredictDivergence, kfold.PredictInformationTheory, MDscan, kfold.MATCH and kfold.PCA
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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