title: Global Prioritization of Disease Candidate Metabolites Based on a Multi-omics Composite Network.
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.
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
authors.forename: Qianlan
authors.initials: Q
authors.lastname: Yao
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
authors.forename: Yanjun
authors.initials: Y
authors.lastname: Xu
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
authors.forename: Haixiu
authors.initials: H
authors.lastname: Yang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
authors.forename: Desi
authors.initials: D
authors.lastname: Shang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
authors.forename: Chunlong
authors.initials: C
authors.lastname: Zhang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
authors.forename: Yunpeng
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
authors.forename: Zeguo
authors.initials: Z
authors.lastname: Sun
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
authors.forename: Xinrui
authors.initials: X
authors.lastname: Shi
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
authors.forename: Li
authors.initials: L
authors.lastname: Feng
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
authors.forename: Junwei
authors.initials: J
authors.lastname: Han
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Su
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.|School of Medical Informatics, Daqing Campus, Harbin Medical University, 39 Xinyang Road, Harbin 163319, China.
authors.forename: Chunquan
authors.initials: C
authors.lastname: Li
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
authors.forename: Xia
authors.initials: X
authors.lastname: Li
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism*; D001943:Breast Neoplasms / Q000175:diagnosis / Q000378:metabolism*; D005260:Female; D053263:Gene Regulatory Networks; D006801:Humans; D008297:Male; D053858:Metabolic Networks and Pathways; D055442:Metabolome; D010641:Phenotype; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000378:metabolism*; D012372:ROC Curve; D012306:Risk; D012984:Software
chemical_list: D014408:Biomarkers, Tumor