Human Pathway-Based Disease Network.

Area of Science:

• Systems Biology
• Computational Biology
• Bioinformatics

Background:

• Disease-disease similarity networks are crucial for understanding disease origins, molecular mechanisms, and functions.
• Existing networks often rely on genes or symptoms, potentially missing pathway-level connections.

Purpose of the Study:

• To construct a novel Human Pathway-based Disease Network (HPDN) using protein interaction data and pathway databases.
• To explore intrinsic disease interactions and their correlation with shared functional pathways and gene sets.
• To evaluate HPDN's predictive power for novel disease-disease correlations and its utility in systems medicine applications like drug repurposing.

Main Methods:

• Integrated a high-quality protein interaction network with pathway databases.
• Developed the Human Pathway-based Disease Network (HPDN).
• Validated HPDN predictions against the Comparative Toxicogenomics Database (CTD) and biomedical literature.

Main Results:

• Disease similarity strongly correlates with shared functional pathways and gene set interactions within HPDN.
• HPDN demonstrates significant overlap with gene- and symptom-based disease networks.
• HPDN successfully predicted new disease-disease correlations, validated by external databases.
• Identified potential new indications for drugs like prednisone and folic acid through network analysis.

Conclusions:

• The Human Pathway-based Disease Network (HPDN) provides a comprehensive and high-quality resource for understanding disease relationships.
• HPDN facilitates systems medicine research, including the prediction of novel disease associations and drug repurposing opportunities.
• The findings offer potential new therapeutic directions for complex diseases.