CNORfeeder

CNORfeeder infers and extends signaling network models by integrating literature-derived prior knowledge with data-driven links inferred from perturbation experiment datasets.

Key Features:

• Hybrid Network Inference: Integrates literature-constrained signaling networks with data-driven inference to improve completeness and accuracy of signaling models.
• Network Extension from Experimental Data: Identifies and incorporates new signaling interactions inferred from perturbation experiment datasets.
• Protein Interaction–Guided Integration: Uses known physical protein interaction information to guide and validate newly inferred network links.

Scientific Applications:

• Signal Transduction Modeling: Supports reconstruction and refinement of cellular signaling pathways using perturbation-based signaling datasets.
• Cancer Signaling Analysis: Enables identification of missing signaling pathways in disease models such as human liver cancer (HepG2).
• Systems Biology Network Reconstruction: Facilitates integration of experimental signaling data with prior knowledge networks to generate context-specific pathway models.

Methodology:

CNORfeeder integrates perturbation-derived signal transduction datasets with literature-based signaling networks and applies inference methods guided by known protein interaction data to propose and validate additional network links.