Mergeomics

Mergeomics integrates multidimensional omics-disease associations, functional genomics, canonical pathways, and gene-gene interaction networks to generate mechanistic hypotheses.

Key Features:

• Marker Set Enrichment Analysis (MSEA): This component performs enrichment analysis on marker sets derived from omics-disease association data.
• Weighted Key Driver Analysis (wKDA): This component identifies key driver genes within gene-gene interaction networks by incorporating network topology and edge weights.

Scientific Applications:

• Multi-omics Integration: Mergeomics enables the integration of diverse omics data types with disease associations to uncover underlying biological mechanisms.
• Network-based Hypothesis Generation: The framework generates mechanistic hypotheses by combining functional genomics, canonical pathways, and gene-gene interaction networks.

Methodology:

The pipeline consists of two main parts: Marker Set Enrichment Analysis (MSEA) and Weighted Key Driver Analysis (wKDA).