Abstract
Single nucleotide polymorphism (SNP) data are typical high-dimensional and low-sample size (HDLSS) data, and they are extremely complex. In this paper, by using a deep neural network with a loci filter method, multi-level abstract features of SNPs data are obtained. Based on the abstract features, we get the diagnosis results for schizophrenia. It shows that the performance of the deep network is better than those of other methods, i.e., linear SVM with soft margin, SVM with multi-layer perceptron kernel, SVM with RBF kernel, sparse representation based classifier and k-nearest neighbor method. These results indicate that the use of deep networks offers a novel approach to deal with HDLSS problem, especially for the medical data analysis.
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Acknowledgment
This research was supported by NSFC Nos. 11471006 and 11101327, National Science and Technology Program of China (No. 2015DFA81780), the Fundamental Research Funds for the Central Universities (No. xjj2017126) and was partly Supported by HPC Platform, Xi’an Jiaotong University.
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Qiao, C., Shi, Y., Li, B., An, T. (2017). A Novel Diagnosis Method for SZ by Deep Neural Networks. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_43
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DOI: https://doi.org/10.1007/978-3-319-61845-6_43
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