KDDANet

KDDANet uncovers hidden gene interactions that mediate Known Drug-Disease Associations (KDDAs) by analyzing genome-wide functional gene interaction networks to inform disease mechanisms and drug repurposing.

Key Features:

• Uncovering Hidden Genes: Reveals genes that escape experimental detection by leveraging genome-wide functional gene interaction networks to mediate KDDAs.
• Competitive Performance: Demonstrates competitive sensitivity and specificity compared with existing state-of-the-art methods.
• Graph-Based Methodology: Employs minimum cost optimization and graph clustering within a unified flow network model to identify mediating gene interactions and modules.
• Case Studies — Alzheimer’s disease and Obesity: Produces predictions with mechanistic relevance in studies of Alzheimer’s disease (AD) and obesity.
• Case Studies — Cancer Omics: Validates known cancer-associated genes and identifies novel candidate genes when applied to multiple types of cancer-omics datasets.
• Discovery of Shared Genes: Identifies genes that mediate multiple KDDAs, enabling cross-disease interaction analysis.

Scientific Applications:

• Disease Pathogenesis: Aids elucidation of molecular mechanisms underlying diseases by identifying hidden mediating genes in drug-disease associations.
• Drug Repurposing: Provides interaction-based insights that can guide repurposing of existing drugs for new therapeutic applications.
• Cancer Research: Supports validation of known genetic associations and discovery of novel cancer-related genes using cancer-omics datasets.

Methodology:

KDDANet leverages genome-wide functional gene interaction networks and applies minimum cost optimization and graph clustering within a unified flow network model.