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 language | English |
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Pages (from-to) | 2574-2576 |
Number of pages | 3 |
Journal | Bioinformatics |
Volume | 31 |
Issue number | 15 |
DOIs | |
Publication status | Published - 1 Aug 2015 |
Externally published | Yes |
Keywords
- Algorithms
- Artificial Intelligence
- Computer Simulation
- HLA-A2 Antigen/metabolism
- Humans
- Models, Theoretical
- Peptide Fragments/metabolism
- Sequence Analysis, Protein/methods
- Software
- Support Vector Machine