Netpredictor

Netpredictor predicts missing links in unipartite and bipartite networks to infer protein interactions and functional relationships within interactomes.

Key Features:

• Link Prediction: Implements Random Walk with Restart (RWR) and network inference algorithms to predict missing connections in unipartite and bipartite networks.
• Network Properties Computation: Computes properties specific to bipartite networks to characterize network structure.
• Community Visualization: Identifies and visualizes communities across two distinct sets of nodes to reveal modular organization.
• Significant Interaction Calculation: Performs permutation-based testing to assess statistical significance of interactions between node sets.
• Pathway Analysis: Searches for top-K shortest paths between nodes in an interactome to explore potential interaction routes.
• Enrichment Analysis: Conducts enrichment analyses for disease, pathways, and ontology to interpret biological relevance of predicted interactions.

Scientific Applications:

• Protein interaction inference: Predicts putative protein–protein and protein–entity links within interactomes to aid mapping of interaction networks.
• Pathway exploration: Identifies shortest-path routes and candidate pathways connecting network nodes for pathway analysis.
• Disease and functional enrichment: Associates predicted interactions with disease terms, pathways, and ontologies to prioritize biologically relevant links.
• Systems biology and drug discovery: Supports network-level analyses that can inform systems biology studies and the identification of candidate targets or mechanisms.

Methodology:

Uses Random Walk with Restart (RWR) and network inference algorithms for link prediction; computes bipartite network properties; applies permutation-based testing for interaction significance; searches top-K shortest paths; and performs enrichment analyses for disease, pathways, and ontology.