Gene Selection for Tumor Classification Using Microarray Gene Expression Data

– In this paper we perform a t-test for significant gene expression analysis in different dimensions based on molecular profiles from microarray data, and compare several computational intelligent techniques for classification accuracy on Leukemia, Lymphoma and Prostate cancer datasets of broad institute and Colon cancer dataset from Princeton gene expression project. This paper also describes results concerning the robustness and generalization capabilities of kernel methods in classifying. We use traditional support vector machines (SVM), biased support vector machine (BSVM) and leave-one-out model selection for support vector machines (looms) for model selection. We also evaluate the impact of kernel type and parameter values on the accuracy of a support vector machine (SVM) performing tumor classification. Through a variety of comparative experiments, it is found that SVM performs the best for detecting Leukemia and Lymphoma, BSVM performs the best for Colon and Prostate cancers....

• Type: Conference
• Year: 2007
• Where: WCE
• Authors: Krishna Yendrapalli, Ram B. Basnet, Srinivas Mukkamala, Andrew H. Sung
• Keywords: Classification Accuracy, Parameter Values, Support Vector Machine, WCE 2007, WCE 2008