Evidence map›Paper›PMID 33108388›Full record

ArticlePloS one2020

Discrimination of alcohol dependence based on the convolutional neural network.

Fangfang Chen, Meng Xiao, Cheng Chen, Chen Chen, Ziwei Yan, Huijie Han, Shuailei Zhang, Feilong Yue, Rui Gao, Xiaoyi Lv

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Fangfang ChenCollege of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China.
Meng XiaoThe Fourth People's Hospital of Urumqi, Urumqi, Xinjiang, China.
Cheng ChenCollege of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China.ORCID 0000-0002-6739-1937
Chen ChenCollege of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China.
Ziwei YanCollege of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China.
Huijie HanSchool of Pharmacy, Shanghai Jiao Tong University, Shanghai, China.
Shuailei ZhangCollege of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China.
Feilong YueCollege of Software, Xinjiang University, Urumqi, Xinjiang, China.
Rui GaoCollege of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China.
Xiaoyi LvCollege of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China.ORCID 0000-0001-6855-7428

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Neural Networks, ComputerAdultAge FactorsAlcoholismEducational StatusFemaleGenotypeHumansLinear ModelsMaleMarital StatusMiddle AgedSupport Vector MachineYoung Adult

Identifiers

PMID33108388
PMCPMC7591038

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.