[Combining protein-protein interactions information with support vector machine to identify chronic obstructive...

Chronic obstructive pulmonary disease (COPD) is a complex human disease with a higher mortality. So far, the studies to COPD have not been well characterized despite the well-documented role of cigarette smoking in the genesis of COPD. In recent years, microarray analyses have help to identify some potential disease related genes. However, the low reproducibility of many published gene signatures has been criticized. It therefore suggested that incorporation of network or pathway information into prognostic biomarker discovery might improve the prediction performance. In this analysis, we combined protein-protein interactions (PPI) information with support vector machine (SVM) method to identify potential COPD-related genes that would allow one to distinguish accurately severe emphysema from non-/mildly emphysematous lung tissue. We identified 8 COPD-related feature genes. When compared with another SVM method which did not use the prior PPI information, the prediction accuracy was significantly enhanced (AUC is increased from 0.513 to 0.909). On the base of results obtained one can suppose that incorporating network of prior knowledge into gene selection methods significantly improves classification accuracy. Consequently, the gene expression profiles from human emphysematous lung tissue may provide insight into pathogenesis, and a good classify prediction algorithm based on prior biology knowledge can further strengthen this performance.