exprso

exprso implements supervised machine learning workflows in R for feature selection, classification (including binary and multi-class via 1-vs-all), regression, ensemble methods, parameter optimization (grid-searching), and cross-validation (including Monte Carlo and nested) to analyze high-dimensional biological datasets such as genomics and proteomics.

Key Features:

• Object-Oriented Framework: Employs an object-oriented, modular design that encapsulates analytical components.
• Feature Selection: Provides modules for selecting relevant features from high-dimensional datasets.
• Classification and Regression: Supports binary and multi-class classification using a 1-vs-all approach and regression for continuous outcomes.
• Ensemble Classification: Integrates ensemble methods to combine multiple models for improved predictive performance.
• Parameter Optimization: Performs high-throughput parameter grid-searching to identify optimal model parameters.
• Cross-Validation Schemes: Supports Monte Carlo and nested cross-validation schemes to assess model robustness and generalizability.
• High-Dimensional Data Handling: Handles high-dimensional datasets typical of genomics and proteomics analyses.

Scientific Applications:

• Genomics: Applied to analysis of genomics datasets such as gene expression for predictive modeling and feature selection.
• Proteomics: Applied to proteomics datasets for predictive modeling and feature selection.
• High-Dimensional Omics Modeling: Used for predictive modeling of continuous and categorical biological outcomes from high-dimensional omics data.

Methodology:

Implements an object-oriented, modular architecture with modules for feature selection, classification (including 1-vs-all), regression, ensemble classification, parameter grid-searching, and cross-validation schemes including Monte Carlo and nested CV.