iNetModels

iNetModels enables integrated analysis and visualization of multi-omics biological networks to elucidate associations across clinical chemistry, anthropometric parameters, plasma proteomics, plasma metabolomics, and oral and gut metagenomic data collected from the same individuals.

Key Features:

• Multi-omics network construction and visualization: Builds and visualizes multi-omics biological networks (MOBNs) integrating clinical chemistry, anthropometric parameters, plasma proteomics, plasma metabolomics, and oral and gut metagenomic data from the same individuals.
• Tissue- and cancer-specific gene co-expression networks (GCNs): Provides tissue- and cancer-specific GCNs for investigating gene interactions within defined biological contexts.
• Context-specific association analysis: Supports exploration of associations between individual features and other omics layers, including stratified analyses such as male/female-specific comparisons.
• Inter-omic relationship analysis: Analyzes and visualizes intricate relationships within and between omics layers to reveal altered biological processes.

Scientific Applications:

• Integrative discovery of altered biological processes: Integrates multi-omics layers to identify molecular signatures and altered pathways associated with health and disease states.
• Cancer research: Uses tissue- and cancer-specific GCNs to study tissue-specific gene interactions and disease mechanisms in cancer.
• Microbiome–host interaction studies: Facilitates investigation of associations between oral and gut metagenomes and host proteomic, metabolomic, and clinical phenotypes.
• Stratified and context-dependent analyses: Enables comparisons across contexts such as sex-specific differences to uncover context-dependent network alterations.

Methodology:

Collection and integration of multi-omics data from individual subjects, construction and analysis of multi-omics biological networks (MOBNs), and generation of tissue- and cancer-specific gene co-expression networks (GCNs) with visualization of inter-omic associations.