Discrimination of alcohol dependence based on the convolutional neural network.

Journal: PloS one
Published Date: Jan 1, 2020

Abstract

In this paper, a total of 20 sites of single nucleotide polymorphisms (SNPs) on the serotonin 3 receptor A gene (HTR3A) and B gene (HTR3B) are used for feature fusion with age, education and marital status information, and the grid search-support vector machine (GS-SVM), the convolutional neural network (CNN) and the convolutional neural network combined with long and short-term memory (CNN-LSTM) are used to classify and discriminate between alcohol-dependent patients (AD) and the non-alcohol-dependent control group. The results show that 19 SNPs combined with academic qualifications have the best discrimination effect. In the GS-SVM, the area under the receiver operating characteristic (ROC) curve (AUC) is 0.87, the AUC of CNN-LSTM is 0.88, and the performance of the CNN model is the best, with an AUC of 0.92. This study shows that the CNN model can more accurately discriminate AD than the SVM to treat patients in time.

Authors

  • Fangfang Chen
    College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China.
  • Meng Xiao
    Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China.
  • Cheng Chen
    Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China.
  • Chen Chen
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
  • Ziwei Yan
    College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China.
  • Huijie Han
    School of Pharmacy, Shanghai Jiao Tong University, Shanghai, China.
  • Shuailei Zhang
    College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China.
  • Feilong Yue
    College of Software, Xinjiang University, Urumqi, Xinjiang, China.
  • Rui Gao
    School of Control Science and Engineering, Shandong University, Jinan, China.
  • Xiaoyi Lv
    College of Information Science and Engineering, Xinjiang University, Urumqi, China.

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