Name Mode Size
AllClasses.R 100644 37 kb
AllGenerics.R 100644 9 kb
access-methods.R 100644 54 kb
annotationSpecificKernel.R 100644 17 kb
coerce-methods.R 100644 8 kb
explicitRepresentation.R 100644 27 kb
featureWeights.R 100644 21 kb
gappyPair.R 100644 25 kb
gridSearch.R 100644 39 kb
heatmap-methods.R 100644 12 kb
kbsvm-methods.R 100644 51 kb
kebabs.R 100644 9 kb
kebabsData.R 100644 3 kb
kebabsDemo.R 100644 8 kb
mismatch.R 100644 12 kb
modelSelection.R 100644 24 kb
motif.R 100644 21 kb
parameters.R 100644 26 kb
performCrossValidation-methods.R 100644 38 kb
plot-methods.R 100644 23 kb
positionDependentKernel.R 100644 16 kb
predict-methods.R 100644 33 kb
predictionProfile.R 100644 26 kb
predictsvm-methods.R 100644 7 kb
runtimeMessage.R 100644 1 kb
sequenceKernel.R 100644 11 kb
show-methods.R 100644 33 kb
spectrum.R 100644 23 kb
svm.R 100644 21 kb
svmModel.R 100644 13 kb
symmetricPair.R 100644 10 kb
trainsvm-methods.R 100644 9 kb
utils.R 100644 36 kb
zzz.R 100644 0 kb
# KeBABS - An R Package for Kernel-Based Analysis of Biological Sequences The package provides functionality for kernel-based analysis of DNA, RNA, and amino acid sequences via SVM-based methods. As core functionality, kebabs implements following sequence kernels: spectrum kernel, mismatch kernel, gappy pair kernel, and motif kernel. Apart from an efficient implementation of standard position-independent functionality, the kernels are extended in a novel way to take the position of patterns into account for the similarity measure. Because of the flexibility of the kernel formulation, other kernels like the weighted degree kernel or the shifted weighted degree kernel with constant weighting of positions are included as special cases. An annotation-specific variant of the kernels uses annotation information placed along the sequence together with the patterns in the sequence. The package allows for the generation of a kernel matrix or an explicit feature representation in dense or sparse format for all available kernels which can be used with methods implemented in other R packages. With focus on SVM-based methods, kebabs provides a framework which simplifies the usage of existing SVM implementations in kernlab, e1071, and LiblineaR. Binary and multi-class classification as well as regression tasks can be used in a unified way without having to deal with the different functions, parameters, and formats of the selected SVM. As support for choosing hyperparameters, the package provides cross validation - including grouped cross validation, grid search and model selection functions. For easier biological interpretation of the results, the package computes feature weights for all SVMs and prediction profiles which show the contribution of individual sequence positions to the prediction result and indicate the relevance of sequence sections for the learning result and the underlying biological functions. This is the source code repository. The package can be installed from [Bioconductor]( Further information and installation instructions are also available from Although the package is maintained by Ulrich Bodenhofer, the package itself has been implemented by Johannes Palme.