gINTomics

gINTomics captures directional biological information flow across genomic, transcriptomic, and epigenomic layers through a hierarchical multi-staged computational framework that models molecular modifications by linking genomic alterations to downstream transcriptional consequences.

Key Features:

• Hierarchical Multi-Omics Integration: Follows a multi-staged paradigm reflecting biological hierarchy, modeling molecular modifications by linking genomic alterations to downstream transcriptional consequences.
• Flexible Integration Strategies: Offers flexible integration strategies for different omics combinations.
• Tailored Statistical Modeling: Provides tailored statistical modeling with optional Random Forest for variable selection.
• Genomic Modification Integration: Detects associations between target expression and regulator expression, taking into account genomic modifications such as Copy Number Variations (CNV) and methylation.
• Interactive Visualization: Provides comprehensive interactive visualization through interactive plots and circos diagrams, enabling exploration of complex regulatory relationships across multiple analytical perspectives.

Scientific Applications:

• Regulatory Mechanism Identification: Identifies biologically relevant regulatory mechanisms by capturing directional information flow across genomic, transcriptomic, and epigenomic layers.
• Prognostic Biomarker Discovery: Discovers prognostic biomarkers through hierarchical multi-omics integration, as validated on TCGA ovarian cancer data.
• Multi-Omics Data Exploration: Enables exploration of complex regulatory relationships across multiple analytical perspectives through interactive visualization.

Methodology:

The framework follows a hierarchical multi-staged paradigm with tailored statistical modeling and optional Random Forest for variable selection.