KDDN

KDDN constructs common and differential dependency networks by integrating prior biological knowledge with quantitative molecular profiling data to identify and quantify network rewiring.

Key Features:

• Integration of Prior Knowledge: Combines existing biological knowledge with experimental data to guide network inference.
• Common and Differential Network Construction: Constructs both common and differential dependency networks to represent shared and condition-specific interactions.
• Identification of Network Rewiring: Detects significant rewiring events in network topology across conditions.
• Quantitative Analysis: Estimates model parameters and computes p-values for significant rewiring events.
• Computational Efficiency: Supports parallel computing on multi-core machines to accelerate analysis.
• Versatility in Data Application: Applicable to microarray gene expression datasets and other quantitative molecular profiling data.

Scientific Applications:

• Network Rewiring Analysis: Characterizes condition-dependent changes in molecular interaction networks.
• Gene Expression Studies: Analyzes gene expression data to reveal altered regulatory relationships.
• Disease Progression and Drug Response: Identifies network changes associated with disease states and responses to therapeutics.

Methodology:

KDDN integrates prior knowledge with measured data within a mathematical framework to construct common and differential networks, identifies significant rewiring events, quantifies changes via model parameters and p-values, and supports parallel computing for efficiency.