title: KeBABS: an R package for kernel-based analysis of biological sequences.
abstract: KeBABS provides a powerful, flexible and easy to use framework for KE: rnel- B: ased A: nalysis of B: iological S: equences 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.
authors: Institute of Bioinformatics, Johannes Kepler University, Linz, Austria.
authors: Johannes
authors: J
authors: Palme
authors: Institute of Bioinformatics, Johannes Kepler University, Linz, Austria.
authors: Sepp
authors: S
authors: Hochreiter
authors: Institute of Bioinformatics, Johannes Kepler University, Linz, Austria.
authors: Ulrich
authors: U
authors: Bodenhofer
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D015789:HLA-A2 Antigen / Q000378:metabolism*; D006801:Humans; D008962:Models, Theoretical; D010446:Peptide Fragments / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D060388:Support Vector Machine
chemical_list: C435939:HLA-A*02:01 antigen; D015789:HLA-A2 Antigen; D010446:Peptide Fragments