APODHIN

APODHIN performs integrative analysis of transcriptomics, proteomics, genomics, and metabolomics data within human interactome networks to identify key molecular players and their interconnections relevant to cancer research.

Key Features:

• Meta-Interactome Network Construction: APODHIN constructs meta-interactome networks by integrating human protein-protein interactions, miRNA-target gene regulatory interactions, and transcription factor-target gene regulatory relationships to map proteins, genes, and miRNAs from omics datasets.
• Context-Specific Interaction Network Extraction: The platform filters the meta-interactome to extract biomolecules differentially altered in specific scenarios, such as cancer, yielding context-specific interaction networks.
• Identification of Topologically Important Nodes (TINs): APODHIN employs graph theory-based network topology analysis to identify TINs including hubs, central nodes, and bottleneck nodes.
• Cross-Pathway Regulatory Link Identification: A module identifies cross-pathway regulatory and protein-protein interaction links connecting signaling proteins, transcription factors, miRNAs, and metabolic enzymes using single-omics and/or pan-omics data combined with mathematical modeling.
• Cancer-Specific Database Component: APODHIN provides a database of cancer-specific meta-interactome networks, TINs, and cross-pathway links for cervical, ovarian, and breast cancers.

Scientific Applications:

• Cancer molecular mechanism analysis: Elucidates interplay between oncogenes, tumor suppressors, signaling pathways, and metabolic enzymes to study disease-specific molecular interactions and metabolic reprogramming.
• Biomarker and therapeutic target discovery: Identifies potential diagnostic and prognostic biomarkers and candidate therapeutic targets via TINs and cross-pathway regulatory links.

Methodology:

Constructing a meta-interactome by integrating human protein-protein interactions, miRNA-target and transcription factor-target regulatory relationships; mapping transcriptomics, proteomics, genomics, and metabolomics data onto the meta-interactome; filtering networks for differentially altered biomolecules; applying graph theory-based network topology analysis to identify TINs; and using mathematical modeling to uncover cross-pathway regulatory links.