Gene Selection and Classification of Human Lymphoma from Microarray Data

Experiments in DNA microarray provide information of thousands of genes, and bioinformatics researchers have analyzed them with various machine learning techniques to diagnose diseases. Recently support vector machines (SVM) have been demonstrated as an effective tool in analyzing microarray data. Previous work involving SVM used every gene in the microarray to classify normal and malignant lymphoid tissue [21]. This paper shows that, using gene selection techniques that selected only 10% of the genes in \´Lymphochip" (a DNA microarray developed at Stanford University School of Medicine), a classification accuracy of about 98% is achieved which is a comparable performance to using every gene. This paper thus demonstrates the usefulness of feature selection techniques in conjunction with SVM to improve its performance in analyzing Lymphochip microarray data. The improved performance was evident in terms of better accuracy, ROC (receiver operating characteristics) analysis and faster training. Using the subsets of Lymphochip, this paper then compared the performance of SVM against two other well-known classifiers: multi-layer perceptron (MLP) and linear discriminant analysis (LDA). Experimental results show that SVM outperforms the other two classifiers.