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BioMed Research International
Volume 2014, Article ID 325697, 14 pages
http://dx.doi.org/10.1155/2014/325697
Research Article

MPINet: Metabolite Pathway Identification via Coupling of Global Metabolite Network Structure and Metabolomic Profile

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

Received 1 April 2014; Accepted 18 May 2014; Published 25 June 2014

Academic Editor: Li-Ching Wu

Copyright © 2014 Feng Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Supplementary Material

Figure S1: Validation of bias via analyzing the 3189 human pathways in the ConsensusPathDB database.

Figure S2: Tryptophan metabolism pathway identified by MPINet, in which the differential metabolites of prostate cancer metastasis were annotated.

Supplementary text: Detailed description of calculating the global connection strength (GCS).

Table S1: The type 2 diabetes associated metabolites from text mining and HMDB database.

Table S2: The detailed information of human metabolite background from five databases.

Table S3: The statistically significant pathways identified by MPINet method for differential metabolites from metastatic prostate cancer dataset (FDR<0.01).

Table S4: The twenty-one pathways identified by MPINet method for interesting metabolites from the type 2 diabetes dataset 1 (FDR<0.01).

  1. Supplementary Material