Distance Metric Learning and Support Vector Machines for Classification of Mass Spectrometry Proteomics Data

Mass spectrometry becomes the most widely used measurement in proteomics research. High dimensionality of features and small dataset are two major limitations restrict the accuracy of classification in mass spectrum data analysis. To improve the data mining result, two major issues need to be highlighted, which are feature extraction and feature selection. 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 apply support vector machine recursive feature elimination (SVMRFE) to choose feature sets for classification. In this article, we successfully 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 the best results by applying SVM to the feature sets chosen with the use of SVMRFE.