The effective diagnosis of schizophrenia by using multi-layer RBMs deep networks

Abstract

Schizophrenia is one of the most prevalent mental diseases, and is considered to be caused by the interplay of a number of genetic factors. In this paper, by constructing a multilayer restricted Boltzmann machines (RBMs) deep network, we use the genomic data (i.e., SNP data) for unsupervised feature learning and disease diagnosis of schizophrenia. In order to obtain some more accurate diagnosis results by RBMs, firstly, we transform the SNP data into binary sequences, and then by training the multi-layer RBMs deep network on unlabeled data, the multi-level abstract features of the genomic data are obtained and stored in the network. Finally, by adding a linear classifier to the top of the multi-layer RBMs deep network, the classification results on the testing data are gained. The results show that the average performance of this method is better than that of other methods, e.g., SVM (including linear SVM as well as SVM with multilayer perceptron kernel), sparse representations based classifier and k-nearest neighbors method. It is indicated that the multi-layer RBMs deep network can extract deep hierarchical representations of the genomic data, and then promises a more comprehensive approach for the mental disease diagnosis.