Lung cancer gene expression database analysis incorporating prior knowledge with support vector machine-based classification method

doi:10.1186/1756-9966-28-103

Journal of Experimental & Clinical Cancer Research

Volume 28

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Lung cancer gene expression database analysis incorporating prior knowledge with support vector machine-based classification method

Peng Guan¹^,2 , Desheng Huang¹^,2 , Miao He³ and Baosen Zhou¹^,2

¹Department of Epidemiology, School of Public Health, China Medical University, Shenyang 110001, PR China

²Key Laboratory of Cancer Etiology and Intervention, University of Liaoning Province, Shenyang 110001, PR China

³Information Center, the First Affiliated Hospital, China Medical University, Shenyang 110001, PR China

author email corresponding author email

Journal of Experimental & Clinical Cancer Research 2009, 28:103doi:10.1186/1756-9966-28-103


Published:	18 July 2009

Abstract

Background

A reliable and precise classification is essential for successful diagnosis and treatment of cancer. Gene expression microarrays have provided the high-throughput platform to discover genomic biomarkers for cancer diagnosis and prognosis. Rational use of the available bioinformation can not only effectively remove or suppress noise in gene chips, but also avoid one-sided results of separate experiment. However, only some studies have been aware of the importance of prior information in cancer classification.

Methods

Together with the application of support vector machine as the discriminant approach, we proposed one modified method that incorporated prior knowledge into cancer classification based on gene expression data to improve accuracy. A public well-known dataset, Malignant pleural mesothelioma and lung adenocarcinoma gene expression database, was used in this study. Prior knowledge is viewed here as a means of directing the classifier using known lung adenocarcinoma related genes. The procedures were performed by software R 2.80.

Results

The modified method performed better after incorporating prior knowledge. Accuracy of the modified method improved from 98.86% to 100% in training set and from 98.51% to 99.06% in test set. The standard deviations of the modified method decreased from 0.26% to 0 in training set and from 3.04% to 2.10% in test set.

Conclusion

The method that incorporates prior knowledge into discriminant analysis could effectively improve the capacity and reduce the impact of noise. This idea may have good future not only in practice but also in methodology.