{ "_id": "6ae74f78818c9e3ef6dbbdab", "Package": "kebabs", "Type": "Package", "Title": "Kernel-Based Analysis of Biological Sequences", "Version": "1.47.1", "Date": "2026-09-17", "Authors@R": "c(person(given = \"Johannes\",\nfamily = \"Palme\",\nrole = c(\"aut\")),\nperson(given = \"Ulrich\",\nfamily = \"Bodenhofer\",\nrole = c(\"aut\", \"cre\", \"ths\"),\nemail = \"ulrich@bodenhofer.com\"))", "Maintainer": "Ulrich Bodenhofer ", "Description": "The package provides functionality for kernel-based\nanalysis of DNA, RNA, and amino acid sequences via SVM-based\nmethods. As core functionality, kebabs implements following\nsequence kernels: spectrum kernel, mismatch kernel, gappy pair\nkernel, and motif kernel. Apart from an efficient\nimplementation of standard position-independent functionality,\nthe kernels are extended in a novel way to take the position of\npatterns into account for the similarity measure. Because of\nthe flexibility of the kernel formulation, other kernels like\nthe weighted degree kernel or the shifted weighted degree\nkernel with constant weighting of positions are included as\nspecial cases. An annotation-specific variant of the kernels\nuses annotation information placed along the sequence together\nwith the patterns in the sequence. The package allows for the\ngeneration of a kernel matrix or an explicit feature\nrepresentation in dense or sparse format for all available\nkernels which can be used with methods implemented in other R\npackages. With focus on SVM-based methods, kebabs provides a\nframework which simplifies the usage of existing SVM\nimplementations in kernlab, e1071, and LiblineaR. Binary and\nmulti-class classification as well as regression tasks can be\nused in a unified way without having to deal with the different\nfunctions, parameters, and formats of the selected SVM. As\nsupport for choosing hyperparameters, the package provides\ncross validation - including grouped cross validation, grid\nsearch and model selection functions. For easier biological\ninterpretation of the results, the package computes feature\nweights for all SVMs and prediction profiles which show the\ncontribution of individual sequence positions to the prediction\nresult and indicate the relevance of sequence sections for the\nlearning result and the underlying biological functions.", "URL": "https://github.com/UBod/kebabs", "License": "GPL (>= 2.1)", "Collate": "AllClasses.R AllGenerics.R access-methods.R svmModel.R\nkebabs.R kebabsData.R runtimeMessage.R parameters.R\nsequenceKernel.R annotationSpecificKernel.R\npositionDependentKernel.R spectrum.R mismatch.R gappyPair.R\nmotif.R explicitRepresentation.R coerce-methods.R\nfeatureWeights.R heatmap-methods.R kbsvm-methods.R\nperformCrossValidation-methods.R gridSearch.R modelSelection.R\ntrainsvm-methods.R predictsvm-methods.R predict-methods.R\npredictionProfile.R plot-methods.R kebabsDemo.R show-methods.R\nsymmetricPair.R svm.R utils.R zzz.R", "VignetteBuilder": "knitr", "biocViews": 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