ArticleScientific reports2015
Global Prioritization of Disease Candidate Metabolites Based on a Multi-omics Composite Network.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 44 citations in OpenAlex.
- Identification of metabolite-disease associations based on knowledge graph.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Patterns of infant fecal metabolite concentrations and social behavioral development in toddlers.Pediatric research · 2024Article
- Multi-omics profiling approach in food allergy.The World Allergy Organization journal · 2023Review
- Metabolite-disease interaction prediction based on logistic matrix factorization and local neighborhood constraints.Frontiers in psychiatry · 2023Article
- Integrated Analysis of Gut Microbiome and Lipid Metabolism in Mice Infected with Carbapenem-ResistantMetabolites · 2022Article
- Deciphering the Core Metabolites of Fanconi Anemia by Using a Multi-Omics Composite Network.Journal of microbiology and biotechnology · 2022Article
- Interpretation of network-based integration from multi-omics longitudinal data.Nucleic acids research · 2022Article
- A Multi-Omics Study Revealing the Metabolic Effects of Estrogen in Liver Cancer Cells HepG2.Cells · 2021Article
- Multi-omics integration in biomedical research - A metabolomics-centric review.Analytica chimica acta · 2021Review
- Network Pharmacology and Metabolomics Studies on Antimigraine Mechanisms of Da Chuan Xiong Fang (DCXF).Evidence-based complementary and alternative medicine : eCAM · 2021Article
- An Inductive Logistic Matrix Factorization Model for Predicting Drug-Metabolite Association With Vicus Regularization.Frontiers in microbiology · 2021Article
- Construction and Analysis of Human Diseases and Metabolites Network.Frontiers in bioengineering and biotechnology · 2020Article
- Prioritizing candidate diseases-related metabolites based on literature and functional similarity.BMC bioinformatics · 2019Article
- Identification of lung-adenocarcinoma-related long non-coding RNAs by random walking on a competing endogenous RNA network.Annals of translational medicine · 2019Article
- Identification of atherosclerosis-related prioritizing metabolites based on a multi-omics composite network.Experimental and therapeutic medicine · 2019Article
- Combined metabolic, phenomic and genomic data to prioritize atrial fibrillation-related metabolites.Experimental and therapeutic medicine · 2019Article
- Prioritization of candidate metabolites for postmenopausal osteoporosis using multi-omics composite network.Experimental and therapeutic medicine · 2019Article
- Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods.International journal of molecular sciences · 2019Article
- Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis.Frontiers in genetics · 2019Review
- Identification of Key Metabolites for Acute Lung Injury in Patients with Sepsis.Iranian journal of public health · 2019Article
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Authors and funding
13 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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