Molecular networks in Network Medicine: Development and applications

Area of Science:

• Translational, Genomic, and Systems Medicine
• Analytical and Computational Methods
• Mechanistic Models

Background:

• Network Medicine integrates network science with Omics Big Data to investigate disease pathogenesis.
• Various analytical methods, including protein-protein interaction, correlation-based, gene regulatory, and Bayesian networks, are employed to infer molecular networks.
• Omics Big Data encompasses genetics, epigenetics, transcriptomics, metabolomics, and proteomics.

Purpose of the Study:

• To discuss molecular data types used in molecular network analyses.
• To survey analytical methods for inferring molecular networks.
• To review efforts in validating and visualizing molecular networks.

Main Methods:

• Application of network science approaches to Omics Big Data.
• Utilizing computational biology tools for integrated analysis.
• Employing diverse analytical methods for molecular network inference.

Main Results:

• Network Medicine has shown successful applications in diseases like pulmonary arterial hypertension, coronary heart disease, and diabetes mellitus, as well as in drug development.
• Identified knowledge gaps include the incompleteness of the molecular interactome and challenges in identifying key genes.
• Limited applications to human diseases remain an area for future development.

Conclusions:

• Network Medicine offers potential improvements in the diagnosis, prognosis, and treatment of complex diseases.
• Further research is needed to address the incompleteness of the molecular interactome and expand applications in human diseases.
• Validation and visualization of molecular networks are crucial for advancing the field.