CaNDis

CaNDis integrates causal biological interaction networks with disease and FDA-approved drug data to construct disease-disease networks and identify candidate genes involved in disease co-occurrence and drug-drug interactions.

Key Features:

• Causal interaction integration: Integrates causal biological interaction networks that represent cellular regulatory pathways.
• Disease and drug incorporation: Expands interaction networks by incorporating disease annotations and FDA-approved drug data.
• Disease-disease network construction: Constructs disease-disease networks in which links represent similarity between diseases.
• Candidate gene identification: Identifies candidate genes that may play known or novel roles in disease co-occurrence and drug-drug interactions.
• Multi-dataset merging: Merges causal interaction data with extensive biological datasets to enable multifaceted exploration of disease relationships.
• Pharmacological connection analysis: Enables analysis of potential pharmacological connections and genes that influence drug-drug interactions.

Scientific Applications:

• Disease mechanism analysis: Elucidates cellular regulatory pathways and mechanisms underlying diseases using causal interactions.
• Gene candidate discovery: Identifies known or novel genes implicated in the co-occurrence of diseases.
• Drug-drug interaction analysis: Investigates genes and network relationships that may mediate drug-drug interactions.
• Targeted drug discovery: Supports identification of therapeutic targets and opportunities for drug repurposing based on network context.
• Disease network analysis: Enables comparative analysis of disease similarity through network-based relationships.

Methodology:

CaNDis integrates causal biological interaction networks with disease annotations and FDA-approved drug data to expand interaction networks, construct disease-disease similarity networks, and identify candidate genes associated with disease co-occurrence and drug-drug interactions.