mND

mND quantifies gene relevance in multi-omics datasets by propagating molecular alteration signals across biological networks using a multi-layer network diffusion framework.

Key Features:

• Network Diffusion Approach: Employs network diffusion to assess gene relevance by considering gene proximity and interactions with neighboring genes within biological networks.
• Gene Score (mND): Implements a gene score (mND) that quantifies a gene's relevance based on its network position and its first neighbors' proximity to other altered genes.
• Performance: Shows improved performance compared to existing methods in identifying genes altered in one or more omics layers and in recovering known cancer-related genes.
• Flexibility and Applicability: Processes diverse input types including multi-omics datasets and stratified classes such as cell clusters derived from single-cell analyses.

Scientific Applications:

• Disease Research: Provides insights into molecular events associated with human diseases to support understanding of disease mechanisms and identification of potential therapeutic targets.
• Oncology: Facilitates cancer genomics research by recovering known cancer genes and analysing gene interactions relevant to oncological studies.

Methodology:

Integrates multi-layer data inputs with network-based analysis and quantifies gene relevance via a diffusion process across biological networks.