MPINet : Metabolite Pathway Identification via Coupling of Global Metabolite Network Structure and Metabolomic Profile
Joint Authors
Gu, Qi
Feng, Li
Shi, Xinrui
Han, Junwei
Li, Feng
Yao, Qianlan
Li, Jing
Xu, Yanjun
Sun, Zeguo
Yang, Haixiu
Zhang, Chunlong
Li, Xia
Su, Fei
Shang, Desi
Ma, Jiquan
Liu, Wei
Zhang, Yunpeng
Li, Chunquan
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-25
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Li, Feng& Xu, Yanjun& Shang, Desi& Yang, Haixiu& Liu, Wei& Han, Junwei…[et al.]. 2014. MPINet : Metabolite Pathway Identification via Coupling of Global Metabolite Network Structure and Metabolomic Profile. BioMed Research International،Vol. 2014, no. 2014, pp.1-14.
https://search.emarefa.net/detail/BIM-463665
Modern Language Association (MLA)
Li, Feng…[et al.]. MPINet : Metabolite Pathway Identification via Coupling of Global Metabolite Network Structure and Metabolomic Profile. BioMed Research International No. 2014 (2014), pp.1-14.
https://search.emarefa.net/detail/BIM-463665
American Medical Association (AMA)
Li, Feng& Xu, Yanjun& Shang, Desi& Yang, Haixiu& Liu, Wei& Han, Junwei…[et al.]. MPINet : Metabolite Pathway Identification via Coupling of Global Metabolite Network Structure and Metabolomic Profile. BioMed Research International. 2014. Vol. 2014, no. 2014, pp.1-14.
https://search.emarefa.net/detail/BIM-463665
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-463665