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Biomed Res Int. 2014;2014:325697. doi: 10.1155/2014/325697. Epub 2014 Jun 25.

MPINet: metabolite pathway identification via coupling of global metabolite network structure and metabolomic profile.

Author information

1
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
2
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China ; Department of Mathematics, Heilongjiang Institute of Technology, Harbin 150050, China.
3
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China ; Department of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
4
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China ; Department of Medical Informatics, Harbin Medical University, Daqing Campus, Daqing 163319, China.

Abstract

High-throughput metabolomics technology, such as gas chromatography mass spectrometry, allows the analysis of hundreds of metabolites. Understanding that these metabolites dominate the study condition from biological pathway perspective is still a significant challenge. Pathway identification is an invaluable aid to address this issue and, thus, is urgently needed. In this study, we developed a network-based metabolite pathway identification method, MPINet, which considers the global importance of metabolites and the unique character of metabolomic profile. Through integrating the global metabolite functional network structure and the character of metabolomic profile, MPINet provides a more accurate metabolomic pathway analysis. This integrative strategy simultaneously captures the global nonequivalence of metabolites in a pathway and the bias from metabolomic experimental technology. We then applied MPINet to four different types of metabolite datasets. In the analysis of metastatic prostate cancer dataset, we demonstrated the effectiveness of MPINet. With the analysis of the two type 2 diabetes datasets, we show that MPINet has the potentiality for identifying novel pathways related with disease and is reliable for analyzing metabolomic data. Finally, we extensively applied MPINet to identify drug sensitivity related pathways. These results suggest MPINet's effectiveness and reliability for analyzing metabolomic data across multiple different application fields.

PMID:
25057481
PMCID:
PMC4095715
DOI:
10.1155/2014/325697
[Indexed for MEDLINE]
Free PMC Article

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