Discrimination of alcohol dependence based on the convolutional neural network.
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 net...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0241268 |
| _version_ | 1818728478433869824 |
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| author | Fangfang Chen Meng Xiao Cheng Chen Chen Chen Ziwei Yan Huijie Han Shuailei Zhang Feilong Yue Rui Gao Xiaoyi Lv |
| author_facet | Fangfang Chen Meng Xiao Cheng Chen Chen Chen Ziwei Yan Huijie Han Shuailei Zhang Feilong Yue Rui Gao Xiaoyi Lv |
| author_sort | Fangfang Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T22:30:38Z |
| format | Article |
| id | doaj.art-6097ec449043482fa4474dff233eaa71 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-17T22:30:38Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-6097ec449043482fa4474dff233eaa712022-12-21T21:30:13ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-011510e024126810.1371/journal.pone.0241268Discrimination of alcohol dependence based on the convolutional neural network.Fangfang ChenMeng XiaoCheng ChenChen ChenZiwei YanHuijie HanShuailei ZhangFeilong YueRui GaoXiaoyi LvIn 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.https://doi.org/10.1371/journal.pone.0241268 |
| spellingShingle | Fangfang Chen Meng Xiao Cheng Chen Chen Chen Ziwei Yan Huijie Han Shuailei Zhang Feilong Yue Rui Gao Xiaoyi Lv Discrimination of alcohol dependence based on the convolutional neural network. PLoS ONE |
| title | Discrimination of alcohol dependence based on the convolutional neural network. |
| title_full | Discrimination of alcohol dependence based on the convolutional neural network. |
| title_fullStr | Discrimination of alcohol dependence based on the convolutional neural network. |
| title_full_unstemmed | Discrimination of alcohol dependence based on the convolutional neural network. |
| title_short | Discrimination of alcohol dependence based on the convolutional neural network. |
| title_sort | discrimination of alcohol dependence based on the convolutional neural network |
| url | https://doi.org/10.1371/journal.pone.0241268 |
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