KeBABS

KeBABS performs kernel-based analysis of DNA, RNA, and amino acid sequences using sequence kernels and support vector machine methodologies to quantify sequence similarity, enable supervised prediction, and provide biologically interpretable pattern weights and prediction profiles.

Key Features:

• Kernel-Based Analysis: Employs sequence kernels to define similarity measures between biological sequences and capture intricate sequence patterns.
• Flexible Framework: Implemented in R and supports various sequence kernels, including those that incorporate sequence annotations and positional information.
• SVM Integration: Integrates three common SVM implementations through a unified interface for model training and prediction.
• Hyperparameter Optimization: Supports hyperparameter selection via cross-validation techniques, including nested cross-validation and grouped cross-validation.
• Biological Interpretation: Computes weights of sequence patterns and generates prediction profiles to highlight contributions of individual sequence positions or sections.

Scientific Applications:

• Genomic Research: Analyzes DNA sequences to identify genetic variations and assess their potential implications.
• Transcriptomics: Studies RNA sequences to investigate gene expression patterns and regulatory mechanisms.
• Proteomics: Examines amino acid sequences to explore protein sequence features relevant to structure–function relationships.

Methodology:

Combines sequence kernels with support vector machines (three SVM implementations via a unified interface), performs hyperparameter selection using cross-validation (including nested and grouped CV), and computes sequence-pattern weights and prediction profiles for interpretation.