TY - GEN
T1 - The effective diagnosis of schizophrenia by using multi-layer RBMs deep networks
AU - Qiao, Chen
AU - Lin, Dong Dong
AU - Cao, Shao Long
AU - Wang, Yu Ping
N1 - Funding Information:
This research was supported by NSFC No. 11101327, No. 11471006 and No. 11171270, and was partially supported by NIH ROI GM109068 and ROI MH104680.
Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/16
Y1 - 2015/12/16
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84962339059&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962339059&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2015.7359751
DO - 10.1109/BIBM.2015.7359751
M3 - Conference contribution
AN - SCOPUS:84962339059
T3 - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
SP - 603
EP - 606
BT - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
A2 - Schapranow, lng. Matthieu
A2 - Zhou, Jiayu
A2 - Hu, Xiaohua Tony
A2 - Ma, Bin
A2 - Rajasekaran, Sanguthevar
A2 - Miyano, Satoru
A2 - Yoo, Illhoi
A2 - Pierce, Brian
A2 - Shehu, Amarda
A2 - Gombar, Vijay K.
A2 - Chen, Brian
A2 - Pai, Vinay
A2 - Huan, Jun
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Y2 - 9 November 2015 through 12 November 2015
ER -