WINNER: A network biology tool for biomolecular characterization and prioritization

Area of Science:

• Network biology
• Computational biology
• Bioinformatics

Background:

• Molecular functions are often inferred using network-based "guilt-by-associations" methods.
• Existing tools like PageRank struggle with noise in gene and protein interaction data.
• Robust methods are needed to expand, filter, and rank molecular entities in disease-specific networks.

Purpose of the Study:

• Introduce Weighted In-Network Node Expansion and Ranking (WINNER), a novel tool for biomolecular network analysis.
• Develop a robust method to rank molecular entities by relevance within biological networks.
• Provide statistical measures to assess the significance of network expansion and node ranking.

Main Methods:

• WINNER accepts molecular interaction network data as input.
• It generates an expanded network with ranked nodes based on relevance.
• Statistical significance is assessed using node-expansion and node-ranking p-values.

Main Results:

• WINNER demonstrated robustness in ranking top molecules through noise-spiking experiments.
• Node degree-preservation randomization yielded normally distributed ranking scores, outperforming other randomization techniques.
• WINNER identified a higher proportion of disease-associated genes compared to PageRank and other tools.
• Case studies (Alzheimer's, breast cancer, etc.) showed WINNER's superior ability to reveal disease biology.

Conclusions:

• WINNER ranking correlates with other methods for high-quality networks.
• The tool provides a robust way to evaluate candidate molecules from high-throughput experiments.
• WINNER enhances biological discovery by improving the analysis of molecular interaction networks.