Fuzzy Neural Network Applications for Gene Selection and Cancer Classification

W. Xie, F. Chu, and L. Wang (Singapore)


Fuzzy Logic and Systems, Neural Networks, t-test, microarray, cancer classification


With the help of gene expression data, types of heterogeneous cancers can be predicted. In this work, we use a t-test-based feature selection method to choose some important genes from thousands of genes. After that, we classify the microarray data sets with a fuzzy neural network (FNN) that we proposed earlier. This FNN combines important features of initial fuzzy model self-generation, parameter optimization and rule-base simplification. We applied this FNN to two well-known gene expression data sets, i.e., the small round blue cell tumor (SRBCT) data set and the leukemia data set. Our results in the two data sets show that the FNN can obtain 100% accuracy with a much smaller number of genes in comparison with previously published methods.

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