DiNetxify

DiNetxify implements three-dimensional disease network analysis for identifying multimorbidity patterns and disease progression pathways from electronic health record data.

Key Features:

• 3D Disease Network Analysis: Provides comprehensive modular functions for executing complete three-dimensional disease network analyses.
• EHR Data Handling: Includes a dedicated data class for handling various electronic health record data formats.
• Visualization: Offers visualization functions for interactive exploration of analysis results.
• Parallel Computing: Supports parallel computing for enhanced performance on large-scale datasets.
• Customizable Parameters: Supports diverse study designs with customizable analysis parameters.
• Large-Scale Optimization: Optimized for large-scale EHR datasets, demonstrated on cohorts exceeding half a million exposed individuals.

Scientific Applications:

• Disease Cluster Identification: Identifies meaningful disease clusters associated with clinical exposures or population characteristics, as demonstrated for short leukocyte telomere length.
• Disease Progression Analysis: Reveals disease progression patterns in general populations, as demonstrated in middle-aged general population studies.
• Multimorbidity Pattern Discovery: Enables comprehensive identification and interpretation of multimorbidity patterns from large-scale EHR data.

Methodology:

The package implements a recently introduced three-dimensional disease network analysis method with dedicated data classes, modular analytical functions, and visualization components.