NIDM: network impulsive dynamics on multiplex biological network for disease-gene prediction

Area of Science:

• Computational Biology
• Systems Biology
• Genomics

Background:

• Disease-gene prediction is crucial but costly and time-consuming via experiments.
• Existing network propagation methods overlook dynamic processes and multi-relational data.

Purpose of the Study:

• To propose a novel framework, Network Impulsive Dynamics on Multiplex biological networks (NIDM), for enhanced disease-gene prediction.
• To leverage dynamical responses and multiplex biological networks for improved accuracy.

Main Methods:

• Developed the NIDM framework with four model variants and four impulsive dynamical signatures (IDSs).
• Identified disease-related genes by analyzing node responses to impulsive signals in multiplex networks.
• Evaluated NIDM performance against four network-based algorithms across diverse biological networks.

Main Results:

• Confirmed the advantage of using multiplex networks and functional associations for disease-gene prediction.
• Demonstrated superior performance of NIDM compared to existing network-based algorithms.
• Provided effective recommendations for NIDM models and IDS signatures.

Conclusions:

• NIDM effectively predicts disease-related genes by integrating diverse biological networks and analyzing network dynamics.
• The developed web server facilitates gene prioritization and analysis, offering a valuable computational tool for researchers.