Priority index

Priority Index prioritizes genes as potential therapeutic targets across immune-mediated diseases using disease-specific genome-wide association studies (GWAS), functional immunogenomics, ontology-based annotation, and protein-protein interaction network evidence.

Key Features:

• Evidence-Based Rating System: Each target gene is assigned an evidence-derived 5-star rating integrating genomic, annotation, and network evidence from disease-specific GWAS, functional immunogenomics, ontologies, and protein-protein interactions.
• Pathway Crosstalk Identification: Identifies networks of highly rated genes that mediate pathway crosstalk and highlights nodal genes connecting molecular pathways.
• Cross-Referencing and Tractability: Cross-references target genes with protein structural data including 3D visualizations of druggable pockets to inform tractability assessments.
• Drug Repurposing Signals: Detects genes highly rated across multiple diseases to indicate drug repurposing opportunities and distinguishes disease-specific target potentials.
• Cross-Disease Comparisons and Faceted Search: Enables cross-disease comparative analyses and faceted search to support integrative mining that leverages human genetics.

Scientific Applications:

• Target Prioritization: Systematically ranks genes for therapeutic targeting in immune-mediated diseases based on integrated genetic and network evidence.
• Pathway and Network Analysis: Reveals molecular pathways, pathway crosstalk, and nodal genes underlying immune-mediated disease biology.
• Drug Discovery and Repurposing: Supports early-stage target selection, tractability assessment using structural pocket data, and identification of drug repurposing opportunities across diseases.

Methodology:

Disease-specific genome-wide association studies (GWAS), functional immunogenomics, ontology-based gene annotation, protein-protein interaction network analysis, pathway crosstalk and nodal-gene identification, an evidence-derived 5-star gene rating system, cross-referencing with protein structural data including 3D druggable-pocket visualization, and cross-disease comparative analyses.