MultiNEP: a multi-omics network enhancement framework for prioritizing disease genes and metabolites simultaneously

Area of Science:

• Bioinformatics
• Systems Biology
• Computational Biology

Background:

• Network information is crucial for identifying disease-associated omics profiles.
• The metabolome links genotypes and phenotypes, gaining research interest.
• Simultaneous prioritization of metabolites and genes using multi-omics networks is challenging due to data imbalance.

Purpose of the Study:

• To develop a framework for simultaneously prioritizing disease-associated metabolites and genes.
• To address the challenge of data imbalance in multi-omics networks.

Main Methods:

• Developed the Multi-omics Network Enhancement Prioritization (MultiNEP) framework.
• Implemented a weighting scheme to reweight sub-network contributions.
• Evaluated the framework using simulation studies and two human cancer cohorts.

Main Results:

• MultiNEP outperforms methods that do not account for network imbalance.
• The framework successfully identifies more true signal genes and metabolites simultaneously.
• Applications to cancer cohorts demonstrate improved prioritization of cancer-related genes by utilizing both within- and between-omics interactions.

Conclusions:

• The MultiNEP framework effectively handles network imbalance in multi-omics data.
• This approach enhances the simultaneous prioritization of disease-associated metabolites and genes.
• The developed R package is publicly available for broader research use.