MCDGPA

MCDGPA prioritizes candidate disease genes by exploiting modular structure and proximity to seed genes within a gene interaction network to produce module-aware global rankings.

Key Features:

• Modular Approach: Hypothesizes that candidate disease genes are proximal to seed genes within the same module of a gene interaction network, restricting predictions to relevant modules.
• Module Partition: Partitions the gene interaction network into distinct modules to isolate disease-associated modules for candidate evaluation.
• Module-specific Gene Prioritization: Prioritizes candidate genes within each module based on their proximity and relationships to seed genes.
• Rank Fusion for Global Ranking: Integrates module-specific rankings via rank fusion to produce a consolidated global ranking of candidate genes.
• Integration of Gene Prioritization Techniques: Supports incorporation of various existing gene prioritization methods within modules.
• Method Robustness: Maintains predictive accuracy regardless of the specific gene prioritization method used within modules or the module partition algorithm employed.

Scientific Applications:

• Prostate cancer networks: Applied to prostate cancer-associated networks, MCDGPA demonstrated improved prediction of disease-related genes compared to previous algorithms.
• Breast cancer networks: Applied to breast cancer-associated networks, MCDGPA demonstrated improved prediction of disease-related genes compared to previous algorithms.
• Cross-network disease gene prediction: Provides robust disease gene prediction across different network configurations and prioritization methods.

Methodology:

Partitions the gene interaction network into modules, prioritizes candidate genes within disease-associated modules based on proximity to seed genes, and fuses module-specific rankings into a global ranking; supports integration of various gene prioritization techniques.