KeyPathwayMiner

KeyPathwayMiner identifies maximal connected sub-networks enriched for dysregulated genes by integrating molecular interaction networks with multi-omics data (DNA microarrays, RNA sequencing, genome-wide methylation) to reveal pathways involved in complex disease mechanisms.

Key Features:

• Integration of Multi-Omics Data: Integrates molecular interaction networks with DNA microarrays, RNA sequencing, and genome-wide methylation data to combine interaction topology with multi-omics measurements.
• Extraction of Maximal Connected Sub-Networks: Identifies all maximal connected sub-networks within a biological network that are enriched for genes showing significant dysregulation, such as differential expression across case-control studies.
• Dynamic Pathway Analysis: Focuses on the dynamic interplay of genes and their products to prioritize pathways affected during disease progression rather than relying solely on static protein-protein interaction networks.

Scientific Applications:

• Disease Mechanism Exploration: Enables identification of disrupted pathways to study mechanisms of complex diseases.
• Biomarker Discovery: Highlights genes with consistent dysregulation patterns across datasets to support biomarker identification for diagnosis and prognosis.
• Therapeutic Target Identification: Pinpoints critical pathway components as candidate therapeutic targets based on network-enriched dysregulation.

Methodology:

Integrates molecular interaction networks with multi-omics datasets (DNA microarrays, RNA sequencing, genome-wide methylation) and extracts maximal connected sub-networks enriched for dysregulated genes, including detection of differential expression across case-control studies.