D-Map: Random Walking on Gene Network Inference Maps Towards differential Avenue Discovery

Area of Science:

• Systems Biology
• Bioinformatics
• Network Medicine

Background:

• Understanding differential rewiring of cellular interaction networks between disease and healthy states is crucial for identifying disease mechanisms.
• A systems-level approach can reveal malfunctioned pathways and key genes involved in disease pathogenesis.

Purpose of the Study:

• To develop and present D-Map, a user-friendly web application for generating and analyzing differential gene interaction networks.
• To enable interactive visualization and interpretation of disease-specific, healthy, and common network states.

Main Methods:

• Utilizing state-of-the-art inference reconstruction methods combined with random walk simulations.
• Inputting gene expression profiles from the Gene Expression Omnibus (GEO) and user-provided gene lists.
• Developing a web application (D-Map) for interactive network generation and analysis.

Main Results:

• D-Map successfully generated and visualized differential networks for various disease states.
• Case studies on Alzheimer's disease and cancers (breast, lung, bladder) demonstrated the methodology's utility.
• Generated interaction lists align with existing literature and offer potential for novel biological insights.

Conclusions:

• D-Map provides a valuable tool for exploring differential gene networks in disease.
• The application facilitates the identification of key players and interaction chains relevant to disease mechanisms.
• Findings support further investigation of D-Map-derived interaction lists for novel therapeutic targets and biological discoveries.