Open AccessDiscrimination of alcohol dependence based on the convolutional neural network
Author(s) -
Fangfang Chen,
Meng Xiao,
Cheng Chen,
Ziwei Yan,
Huijie Han,
Shuailei Zhang,
Feilong Yue,
Rui Gao
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0241268
Subject(s) - convolutional neural network , receiver operating characteristic , artificial intelligence , support vector machine , pattern recognition (psychology) , computer science , single nucleotide polymorphism , alcohol dependence , area under curve , machine learning , alcohol , bioinformatics , biology , gene , genetics , genotype , biochemistry , pharmacokinetics
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.