EnrichNet

EnrichNet applies a graph-based statistic to integrate molecular interaction networks and tissue-specific expression data for enrichment analysis of experimentally derived gene or protein sets.

Key Features:

• Graph-Based Statistic: Employs a graph-based statistic leveraging molecular interaction networks to score functional associations beyond simple overlap, including non-overlapping genes and proteins and those with missing annotations.
• Network Structure Integration: Incorporates the network structure of physical interactions between gene/protein sets to capture relationships not evident from set overlap alone.
• Tissue-Specific Analysis: Accounts for tissue-specific gene/protein set associations using tissue-specific expression data to provide context-dependent enrichment signals.
• Sub-Network Visualization: Generates sub-network visualizations of molecular interactions to support biological interpretation of enriched associations.
• Prioritization of Functional Associations: Prioritizes putative functional gene/protein set associations by exploiting network topology and the density of protein interactions.

Scientific Applications:

• Disease-Associated Gene Set Analysis: Analysis of gene or protein sets implicated in human diseases to uncover associated pathways and interaction patterns.
• Pathway and Interaction Discovery: Identification of novel pathway associations that correspond to dense sub-networks of protein interactions.
• Prioritization for Mechanistic Insights and Targets: Prioritization of candidate functional associations to inform disease mechanisms and potential therapeutic targets.

Methodology:

Combines a graph-based statistic with sub-network visualization, exploiting molecular interaction networks and tissue-specific expression data to prioritize and interpret functional associations.