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

Area of Science:

• Bioinformatics
• Computational Biology
• Machine Learning in Biology

Background:

• Kernel-based methods are powerful for biological sequence analysis.
• Existing tools may lack flexibility or comprehensive interpretation features.
• Support Vector Machines (SVMs) are widely used for sequence classification.

Purpose of the Study:

• To introduce KeBABS, a novel R framework for kernel-based analysis of biological sequences.
• To provide efficient implementations of various sequence kernels, including those considering annotations and positional information.
• To facilitate biological interpretation of machine learning models derived from sequence data.

Main Methods:

• Development of the KeBABS R package.
• Integration of multiple Support Vector Machine (SVM) implementations.
• Implementation of various sequence kernels, including annotation-aware and position-aware variants.
• Cross-validation techniques for hyperparameter selection (CV, nested CV, grouped CV).

Main Results:

• KeBABS provides a unified interface for kernel-based sequence analysis in R.
• Efficient kernel implementations are available, supporting advanced features.
• Integrated hyperparameter optimization using cross-validation strategies.
• Tools for biological interpretation, including pattern weights and prediction profiles, are provided.

Conclusions:

• KeBABS offers a powerful, flexible, and user-friendly framework for biological sequence analysis.
• The framework enhances the interpretability of machine learning models in bioinformatics.
• KeBABS facilitates advanced kernel-based analyses and model interpretation within the R environment.