KeBABS: An R package for kernel-based analysis of biological sequences

Johannes Palme, Sepp Hochreiter, Ulrich Bodenhofer

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

KeBABS provides a powerful, flexible and easy to use framework for kernel-based analysis of biological sequences in R. It includes efficient implementations of the most important sequence kernels, also including variants that allow for taking sequence annotations and positional information into account. KeBABS seamlessly integrates three common support vector machine (SVM) implementations with a unified interface. It allows for hyperparameter selection by cross validation, nested cross validation and also features grouped cross validation. The biological interpretation of SVM models is supported by (1) the computation of weights of sequence patterns and (2) prediction profiles that highlight the contributions of individual sequence positions or sections.

Original languageEnglish
Pages (from-to)2574-2576
Number of pages3
JournalBioinformatics
Volume31
Issue number15
DOIs
Publication statusPublished - 1 Aug 2015
Externally publishedYes

Keywords

  • Algorithms
  • Artificial Intelligence
  • Computer Simulation
  • HLA-A2 Antigen/metabolism
  • Humans
  • Models, Theoretical
  • Peptide Fragments/metabolism
  • Sequence Analysis, Protein/methods
  • Software
  • Support Vector Machine

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