Classification Study on DNA Microarray with Feedforward Neural Network Trained by Singular Value Decomposition

DNA microarray is a multiplex technology used in molecular biology and biomedicine. It consists of an arrayed series of thousands of microscopic spots of DNA oligonucleotides, called features, of which the result should be analyzed by computational methods. Analyzing microarray data using intelligent computing methods has attracted many researchers in recent years. Several approaches have been proposed, in which machine learning based approaches play an important role for biomedical research such as gene expression interpretation, classification and prediction for cancer diagnosis, etc. In this paper, we present an application of the feedforward neural network (SLFN) trained by the singular value decomposition (SVD) approach for DNA microarray classification. The classifier of the single hidden-layer feedforward neural network (SLFN) has the activation function of the hidden units to be ‘tansig’. Experimental results show that the SVD trained feedforward neural network is simple in both training procedure and network structure; it has low computational complexity and can produce better performance with compact network architecture.