PathNet

PathNet identifies pathway-level associations by integrating differential expression data (e.g., microarray) with pathway topology to reveal interconnected and contextually relevant biological pathways.

Key Features:

• Connectivity-Based Analysis: Utilizes structural connections within pathway descriptions to identify enriched pathways and elucidate dependencies and associations among them by considering gene interactions within and across pathways.
• Differential Expression Integration: Integrates differential expression data from high-throughput experiments, such as microarray studies, with pathway topology to strengthen evidence for pathway involvement in specific conditions.
• Pathway Contextual Associations: Scores associations between pathways based on the connectivity of differentially expressed genes across pathways and statistically identifies biological relationships that may be overlooked by conventional enrichment methods.
• Application in Disease Research: Applied to Alzheimer's disease microarray datasets to detect critical pathway deregulations, including ubiquitin-mediated proteolysis, that were not detected by standard enrichment analyses.

Scientific Applications:

• Disease mechanism discovery: Identifies pathway deregulations and hidden inter-pathway relationships relevant to diseases such as Alzheimer's using differential expression integrated with pathway topology.
• Pathway interdependency analysis: Elucidates network-level pathway interdependencies and non-obvious associations for studies requiring understanding of complex biological interactions.

Methodology:

Analyzes gene expression data within the context of pathway topology by considering differential expression levels and the connectivity of genes with neighboring genes in pathways, scoring associations between pathways based on the connectivity of differentially expressed genes and applying statistical evaluation to identify significant pathway relationships.