MOGAMUN

MOGAMUN identifies active modules in multiplex biological networks by using a Multi-Objective Genetic Algorithm to jointly optimize subnetwork interaction density and node scores such as differential expression levels, revealing densely connected and biologically significant gene and protein modules.

Key Features:

• Multi-Objective Optimization: MOGAMUN employs a Multi-Objective Genetic Algorithm (MOGA) to simultaneously optimize interaction density within subnetworks and individual node scores (e.g., differential expression levels).
• Multiplex Network Integration: It leverages multiplex biological networks composed of multiple layers representing different types of physical and functional relationships between genes and proteins, with each layer having distinct meanings, topologies, and biases.
• Comparative Performance: In comparisons with single-network module identification methods, MOGAMUN identifies denser, higher-scoring, and more interpretable modules through integration of multiple network layers.

Scientific Applications:

• FSHD module discovery: Applied to study cellular processes affected by Facio-Scapulo-Humeral muscular Dystrophy (FSHD) by integrating RNA-seq expression data with a multiplex biological network to identify active modules that provide new perspectives on disease pathomechanisms.

Methodology:

MOGAMUN applies a Multi-Objective Genetic Algorithm (MOGA) to jointly optimize subnetwork interaction density and node scores and integrates RNA-seq expression data with multiplex biological networks.