PathWalks

PathWalks implements a random-walk algorithm in R to traverse pathway-to-pathway networks guided by disease-specific gene network maps constructed from multi-source information, identifying pathway communities and functional connections relevant to disease mechanisms.

Key Features:

• Random-Walk Algorithm: Uses a random-walk approach where a virtual walker traverses a pathway-to-pathway network guided by a disease-specific gene map.
• Implementation (R): Implemented in R.
• Integration of Multi-Source Information: Constructs disease-specific gene network maps by integrating diverse data sources relevant to the disease under study.
• Identification of Pathway Communities: Detects pathway communities by analyzing the most frequently traversed trajectories of the walker.
• Application to Disease Studies: Has been applied to Alzheimer's disease and idiopathic pulmonary fibrosis to identify pathway communities.
• Discovery of New Pathways: Reveals additional pathways functionally connected to known ones, providing novel insights for further investigation.

Scientific Applications:

• Disease mechanism analysis: Maps interactions among pathways to elucidate disease mechanisms and how perturbations propagate across pathways.
• Alzheimer's disease: Identifies pathway communities in Alzheimer's disease corroborated by other pathway analysis tools and bibliographic references.
• Idiopathic pulmonary fibrosis: Identifies pathway communities in idiopathic pulmonary fibrosis corroborated by other pathway analysis tools and bibliographic references.
• Hypothesis generation: Reveals novel functionally connected pathways to support experimental follow-up and target exploration.

Methodology:

Constructs a gene network map from multi-source disease-related information; uses that map to guide a random-walk algorithm through a pathway-to-pathway network and identifies frequently traversed pathways that form communities; implemented in R.