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Sci Rep. 2015 Nov 24;5:17201. doi: 10.1038/srep17201.

Global Prioritization of Disease Candidate Metabolites Based on a Multi-omics Composite Network.

Author information

1
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
2
School of Medical Informatics, Daqing Campus, Harbin Medical University, 39 Xinyang Road, Harbin 163319, China.

Abstract

The identification of disease-related metabolites is important for a better understanding of metabolite pathological processes in order to improve human medicine. Metabolites, which are the terminal products of cellular regulatory process, can be affected by multi-omic processes. In this work, we propose a powerful method, MetPriCNet, to predict and prioritize disease candidate metabolites based on integrated multi-omics information. MetPriCNet prioritized candidate metabolites based on their global distance similarity with seed nodes in a composite network, which integrated multi-omics information from the genome, phenome, metabolome and interactome. After performing cross-validation on 87 phenotypes with a total of 602 metabolites, MetPriCNet achieved a high AUC value of up to 0.918. We also assessed the performance of MetPriCNet on 18 disease classes and found that 4 disease classes achieved an AUC value over 0.95. Notably, MetPriCNet can also predict disease metabolites without known disease metabolite knowledge. Some new high-risk metabolites of breast cancer were predicted, although there is a lack of known disease metabolite information. A predicted disease metabolic landscape was constructed and analyzed based on the results of MetPriCNet for 87 phenotypes to help us understand the genetic and metabolic mechanism of disease from a global view.

PMID:
26598063
PMCID:
PMC4657017
DOI:
10.1038/srep17201
[Indexed for MEDLINE]
Free PMC Article

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