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. 2008 Sep;7(9):4013-21.
doi: 10.1021/pr8002886. Epub 2008 Jul 30.

The knowledge-integrated network biomarkers discovery for major adverse cardiac events

Guangxu Jin  1 , Xiaobo Zhou, Honghui Wang, Hong Zhao, Kemi Cui, Xiang-Sun Zhang, Luonan Chen, Stanley L Hazen, King Li, Stephen T C Wong
Affiliations

The knowledge-integrated network biomarkers discovery for major adverse cardiac events

Guangxu Jin et al. J Proteome Res. 2008 Sep.

Abstract

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein-protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein-protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction.

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Figures

Figure 1
Figure 1
The scheme for knowledge integrated biomarker discovery.
Figure 2
Figure 2
Flowchart for cardiovascular-related protein network.
Figure 3
Figure 3
The P-value vector for a protein in different factions of mass spectrometry data.
Figure 4
Figure 4
The best classification accuracy is obtained by multitype biomarker.
Figure 5
Figure 5
The ROC curves for the chosen biomarkers.
Figure 6
Figure 6
The best classification performances for the subnetworks with different proteins.
See this image and copyright information in PMC

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