Applying RBF Neural Networks to Cancer Classification Based on Gene Expressions

Accurate classification of cancers based on microarray gene expressions is very important for doctors to choose a proper treatment. In this paper, we apply a novel radial basis function (RBF) neural network that allows for large overlaps among the hidden kernels of the same class to this problem. We tested our RBF network in three data sets, i.e., the lymphoma data set, the small round blue cell tumors (SRBCT) data set, and the ovarian cancer data set. The results in all the three data sets show that our RBF network is able to achieve 100% accuracy with much fewer genes than the previously published methods did.

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