iDDN

iDDN extends the Differential Dependency Networks (DDN) framework with biologically principled designs to enable robust multi-omics differential network inference, identifying differentially wired molecules responsible for phenotypic transitions in trans-omics regulatory circuitry.

Key Features:

• Integrative Multi-Omics Inference: Extends DDN to enable joint learning of common network structure and rewiring across multiple omics data types.
• Trans-Omics Regulatory Mapping: Identifies differentially wired molecules within study-specific and often unknown trans-omics regulatory circuitry.
• Robust Network Analysis: Addresses limitations of single-omics approaches that are incomplete and prone to collapse under multifactorial trans-omics regulatory mechanisms.

Scientific Applications:

• Disease Network Characterization: Maps gene networks that drive disease progression to identify molecules that rectify networks by normalizing pivotal regulatory elements.
• Therapeutic Target Discovery: Identifies upstream normalizers that represent targets for developing interventions to prevent disease initiation or interrupt progression pathways.
• Phenotypic Transition Analysis: Detects significant rewiring of regulatory network structures under different conditions to understand phenotypic transitions.

Methodology:

iDDN builds upon the Differential Dependency Networks (DDN) method with biologically principled extensions for integrative multi-omics analysis; comparative experimental evaluations were performed on realistic simulations and case studies.