WIPER

WIPER ranks and prioritizes edges in biomolecular association networks by combining in-path traversal scores and statistical significance to characterize gene regulatory associations within biological processes.

Key Features:

• In-path traversal scores: Computes traversal frequency of each edge within paths connecting nodes (genes or proteins) to quantify edge involvement in network paths.
• Statistical significance testing: Integrates statistical tests to assess whether observed edge traversal frequencies deviate from expectation.
• Edge ranking: Generates a rank order of all network edges based on combined in-path traversal scores and statistical significance.
• Edge classification: Distinguishes "well traversed in-path edges" (statistically more traversed than typical edges) from "peripheral in-path edges" (less frequently traversed).
• Comparison to centrality metrics: Provides a traversal-based prioritization that offers a different perspective from traditional metrics such as betweenness centrality.

Scientific Applications:

• Cardiomyocyte regeneration and proliferation: Applied to postanal pig heart gene expression data to highlight signaling pathways implicated in cardiomyocyte regeneration and proliferation.
• Alzheimer's disease genetic associations: Applied to Alzheimer’s disease genetic association data to identify gene–gene associations (e.g., SRC:APP, AR:APP, APP:FYN, APP:NES) found to be statistically and biologically significant with supporting PubMed co-citation data.
• Discovery from high-throughput data: Facilitates identification of critical signaling and regulatory events from high-throughput biology datasets by prioritizing biologically relevant edges.

Methodology:

Evaluate interactions by computing in-path traversal scores for edges, test edge traversal frequencies within paths connecting nodes (genes or proteins), and integrate statistical significance tests to produce a ranked list of edges.