MoRad calculates molecular risk scores using machine learning models and performs enrichment analysis to identify enriched pathways and molecular features. It addresses the need for a streamlined workflow to analyze genomic data and discover potential biomarkers. The package employs R for statistical computing and data analysis.
MoRad provides a comprehensive workflow for molecular risk scoring and enrichment analysis. It offers convenient functions to calculate risk scores and perform enrichment analyses on various molecular features. The package includes visualization tools to present results effectively.
- Risk Scoring: Calculates molecular risk scores using pre-trained machine learning models.
- Enrichment Analysis: Performs Gene Ontology and pathway enrichment analysis on molecular features.
- Data Handling: Accepts standard R data frames as input for molecular feature data.
- Visualization: Offers functions to visualize enrichment results and risk scores.
- Workflow Integration: Designed for seamless integration into R-based genomic analysis pipelines.
- Output Export: Supports exporting enrichment results to CSV files for further analysis.
- Model Flexibility: Utilizes customizable models for risk scoring based on user requirements.
MoRad has been actively maintained and updated, with a consistent release history. It has a moderate level of community engagement, evidenced by issue reporting and contributions. Documentation is available, though further expansion is beneficial. The package appears reliable for basic molecular risk scoring and enrichment analyses.
MoRad benefits genomic researchers seeking efficient tools for molecular risk prediction and pathway identification. It supports real-world use cases in biomarker discovery and disease understanding. MoRad provides a valuable alternative to manual enrichment analysis or custom scripting, accelerating the research process.