A Study on Gene Selection for Cancer Classification Using Radial Basis Function Network
Date Issued
2008
Date
2008
Author(s)
Wu, Howard James
Abstract
Human experts hope to use microarray data to know if a patient has a caner and to identify genes associated with cancer. However, a microarray data has many features (genes), for example, human has more than twenty thousand genes. It is not only a difficult task for human to discover pattern in the microarray data but also a problem for machine learning methods. Therefore, we need to rank the importance of these genes in microarray data in order to select informative genes. And it could not only help human experts to research what genes lead to cancer but also help machine learning methods to increase the accuracy in cancer classification.n this thesis, we studied the impact of feature selection methods on cancer classifier with DNA microarray data sets, especially on radial basis function network (RBF network). The experiment showed that RBF network could achieve similar accuracy with optimized support vector machine (SVM) in much less computing time. By using feature selection methods, RBF network could has more improvement than SVM in cancer classification accuracy.uring the research of feature selection, we observed that noisy genes could affect RBF network more than SVM. We, therefore, proposed a feature selection method, QuickRBF-RFE. QuickRBF could rank the importance of genes by itself and we could select a subset of discriminate genes by recursive feature elimination algorithm. Our experiment result showed that QuickRBF-RFE had similar performance with SVM-RFE in cancer classification. Moreover some of the top genes identified by QuickRBF-RFE, such as Bcl-xl in lymphoma cancer, CXCL10 in prostate cancer, were clarified to be associated with cancer in biological literature, which were difficult to be identified by statistical feature selection methods and SVM-RFE. Moreover we discussed why various feature selection methods would select different genes for cancer classification. We hope our research could open a new direction in cancer research.
Subjects
DNA microarray
Cancer classification
Radial basis function network
Feature selection
Recursive feature elimination
SDGs
Type
thesis
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