ZoomOut

ZoomOut analyzes, clusters, and visualizes multiple biological networks as a unified super network to quantify and compare inter-network relationships.

Key Features:

• Network feature calculation: Calculates various network properties for each input network to serve as quantitative features for analysis.
• Feature-based clustering: Applies clustering algorithms to network-derived features to identify groups of similar networks.
• Super-network representation: Represents individual networks as nodes within a higher-level super network to model inter-network relationships.
• Super-network visualization: Visualizes processed networks as a super network where inter-network connections are informed by clustering distances.
• Concurrent quantification of multiple networks: Quantifies and compares multiple networks concurrently to evaluate both individual and collective properties.

Scientific Applications:

• Systems biology: Comparative analysis of interaction networks to elucidate system-level organization and relationships.
• Genomics: Comparison of gene networks or co-expression networks across conditions or datasets to identify shared or distinct patterns.
• Proteomics: Comparative analysis of protein–protein interaction networks to reveal functional associations and network-level similarities.
• Cross-network functional inference: Identification of patterns, relationships, and potential functional associations that emerge only when networks are analyzed collectively.

Methodology:

Inputting multiple networks; calculating network-specific feature properties; applying clustering algorithms on those features; visualizing results as a super network in which inter-network connections are informed by clustering distances.