MONET

MONET identifies disease modules within molecular networks that are collectively associated with diseases or their risk factors to elucidate disease mechanisms and nominate candidate biomarkers.

Key Features:

• Integration of DMI DREAM Challenge methods: Incorporates the three top-performing methods from the Disease Module Identification (DMI) DREAM Challenge for network modularisation.
• Cross-network module detection: Identifies disease modules across various types of molecular networks.
• Technical implementation: Core algorithms implemented in R, Python, Ada, and C++, with Docker and Singularity containers provided for deployment.

Scientific Applications:

• Systems biology and biomedical research: Enables analysis of modular structure in molecular networks to support investigation of biological organization and disease-related network perturbations.
• Biomarker discovery: Supports identification of network modules whose components are candidate biomarkers for diagnosis, prognosis, or therapeutic targeting.
• Complex disease studies: Facilitates investigation of diseases with multifactorial etiology, including cancer, neurodegenerative disorders, and cardiovascular conditions.

Methodology:

Integrates the three top-performing methods from the DMI DREAM Challenge; core algorithms implemented in R, Python, Ada, and C++; distributed with Docker and Singularity containers.