Distance metric learning and support vector machines for classification of mass spectrometry proteomics data
by Qingzhong Liu, Mengyu Qiao, Andrew H. Sung
International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP), Vol. 1, No. 3, 2009

Abstract: Mass spectrometry has become a widely used measurement in proteomics research. High dimensionality of features and small dataset are two major limitations hindering the accuracy of classification in mass spectrum data analysis; consequently, to obtain good results, the issues of feature extraction and feature selection are especially important. The quality of the feature set determines the reliability of the prediction of disease status. A well-known approach is to detect peak values and then apply support vector machine recursive feature elimination (SVMRFE) to choose feature sets for classification. In this paper, we apply a distance metric learning to classify proteomics mass spectrometry data. Experimental results show that distance metric learning can successfully be applied to the classification of proteomics data and the results are comparable to or better than, the best results by applying SVM to the feature sets chosen with the use of SVMRFE. We also perform feature reduction using manifold learning and experimental results indicate its promising potential in this application.

Online publication date: Sat, 03-Oct-2009

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