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

Research output: Contribution to journalArticlepeer-review

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 - 01 Aug 2015

Fields of science

  • 303 Health Sciences
  • 304 Medical Biotechnology
  • 304003 Genetic engineering
  • 305 Other Human Medicine, Health Sciences
  • 101004 Biomathematics
  • 101018 Statistics
  • 102 Computer Sciences
  • 102001 Artificial intelligence
  • 102004 Bioinformatics
  • 102010 Database systems
  • 102015 Information systems
  • 102019 Machine learning
  • 106023 Molecular biology
  • 106002 Biochemistry
  • 106005 Bioinformatics
  • 106007 Biostatistics
  • 106041 Structural biology
  • 301 Medical-Theoretical Sciences, Pharmacy
  • 302 Clinical Medicine

JKU Focus areas

  • Computation in Informatics and Mathematics
  • Nano-, Bio- and Polymer-Systems: From Structure to Function
  • Medical Sciences (in general)
  • Health System Research
  • Clinical Research on Aging

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