BioNet

BioNet identifies functional modules in protein-protein interaction (PPI) networks by integrating transcriptomic p-values with PPI data using a beta-uniform mixture model and subnetwork optimization in R.

Key Features:

• Integrated Network Analysis: Integrates transcriptomic p-values with protein-protein interaction (PPI) networks by assigning statistical p-values to network nodes.
• Functional Module Identification: Detects functional modules using both exact and heuristic approaches, including integer linear programming for optimal subnetworks.
• Scoring Mechanism: Calculates node and region scores by fitting a beta-uniform mixture (BUM) model to the assigned p-values.
• Network Search and Visualization: Performs network searches to identify high-scoring subnetworks and provides visualization methods for interpreting results.
• Comprehensive Framework: Provides a framework for integrating transcriptomic and functional data with biological networks for systems-level analysis.

Scientific Applications:

• Systems Biology: Supports systems-level analysis of gene and protein interactions by identifying coordinated modules within PPI networks.
• Transcriptomics and Interactomics Integration: Enables combined analysis of transcriptomic data with interactome/PPI data to link differential expression to network structure.
• Disease Mechanisms: Facilitates discovery of subnetworks implicated in disease mechanisms by highlighting dysregulated modules.
• Drug Target Discovery: Aids identification of candidate drug targets through detection of functionally relevant subnetworks.
• Cellular Process Analysis: Reveals modules corresponding to cellular processes and pathways within PPI networks.

Methodology:

Fits a beta-uniform mixture model to node p-values and applies an integer linear programming algorithm, alongside heuristic methods, to identify maximum-scoring subnetworks.