Gene selection and cancer classification using a fuzzy neural network

Cancer classification based on microarray gene expressions is an important problem. 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 three well-known gene expression data sets, i.e., the lymphoma data set (with 3 sub-types), small round blue cell tumor (SRBCT) data set (with 4 sub-types), and the liver cancer data set (with 2 classes, i.e., non-tumor and hepatocellular carcinoma (HCC)). Our results in all the three data sets show that the FNN can obtain 100% accuracy with a much smaller number of genes in comparison with previously published methods. In view of the smaller number of genes required by the FNN and its high accuracy, we conclude that the FNN classifier not only helps biological researchers differentiate cancers that are difficult to be classified using traditional clinical methods, but also helps biological researchers focus on a small number of important genes to find the relationships between those important genes and the development of cancers.