A Prediction Method for Animal-Derived Drug Resistance Trend Using a Grey-BP Neural Network Combination Model
There is an increasing drug resistance of animal-derived pathogens, seriously posing a huge threat to the health of animals and humans. Traditional drug resistance testing methods are expensive, have low efficiency, and are time-consuming, making it difficult to evaluate overall drug resistance. To...
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2021-06-01
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doaj-aedc8da947db4f88977abfc4953777ee2021-06-30T23:44:54ZengMDPI AGAntibiotics2079-63822021-06-011069269210.3390/antibiotics10060692A Prediction Method for Animal-Derived Drug Resistance Trend Using a Grey-BP Neural Network Combination ModelXinxing Li0Ziyi Zhang1Ding Xu2Congming Wu3Jianping Li4Yongjun Zheng5Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaBeijing Advanced Innovation Center for Food Nutrition and Human Health, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaBeijing Advanced Innovation Center for Food Nutrition and Human Health, College of Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Veterinary Medicine, China Agricultural University, Beijing 100083, ChinaBeijing Advanced Innovation Center for Food Nutrition and Human Health, College of Engineering, China Agricultural University, Beijing 100083, ChinaBeijing Advanced Innovation Center for Food Nutrition and Human Health, College of Engineering, China Agricultural University, Beijing 100083, ChinaThere is an increasing drug resistance of animal-derived pathogens, seriously posing a huge threat to the health of animals and humans. Traditional drug resistance testing methods are expensive, have low efficiency, and are time-consuming, making it difficult to evaluate overall drug resistance. To develop a better approach to detect drug resistance, a small sample of Escherichia coli resistance data from 2003 to 2014 in Chengdu, Sichuan Province was used, and multiple regression interpolation was applied to impute missing data based on the time series. Next, cluster analysis was used to classify anti-<i>E. coli</i> drugs. According to the classification results, a GM(1,1)-BP model was selected to analyze the changes in the drug resistance of <i>E. coli</i>, and a drug resistance prediction system was constructed based on the GM(1,1)-BP Neural Network model. The GM(1,1)-BP Neural Network model showed a good prediction effect using a small sample of drug resistance data, with a determination coefficient R<sup>2</sup> of 0.7830 and an RMSE of only 0.0527. This model can be applied for the prediction of drug resistance trends of other animal-derived pathogenic bacteria, and provides the scientific and technical means for the effective assessment of bacterial resistance.https://www.mdpi.com/2079-6382/10/6/692drug resistancemicrobialBP neural networkgrey systemGM(1,1)-BP neural network model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xinxing Li Ziyi Zhang Ding Xu Congming Wu Jianping Li Yongjun Zheng |
spellingShingle |
Xinxing Li Ziyi Zhang Ding Xu Congming Wu Jianping Li Yongjun Zheng A Prediction Method for Animal-Derived Drug Resistance Trend Using a Grey-BP Neural Network Combination Model Antibiotics drug resistance microbial BP neural network grey system GM(1,1)-BP neural network model |
author_facet |
Xinxing Li Ziyi Zhang Ding Xu Congming Wu Jianping Li Yongjun Zheng |
author_sort |
Xinxing Li |
title |
A Prediction Method for Animal-Derived Drug Resistance Trend Using a Grey-BP Neural Network Combination Model |
title_short |
A Prediction Method for Animal-Derived Drug Resistance Trend Using a Grey-BP Neural Network Combination Model |
title_full |
A Prediction Method for Animal-Derived Drug Resistance Trend Using a Grey-BP Neural Network Combination Model |
title_fullStr |
A Prediction Method for Animal-Derived Drug Resistance Trend Using a Grey-BP Neural Network Combination Model |
title_full_unstemmed |
A Prediction Method for Animal-Derived Drug Resistance Trend Using a Grey-BP Neural Network Combination Model |
title_sort |
prediction method for animal-derived drug resistance trend using a grey-bp neural network combination model |
publisher |
MDPI AG |
series |
Antibiotics |
issn |
2079-6382 |
publishDate |
2021-06-01 |
description |
There is an increasing drug resistance of animal-derived pathogens, seriously posing a huge threat to the health of animals and humans. Traditional drug resistance testing methods are expensive, have low efficiency, and are time-consuming, making it difficult to evaluate overall drug resistance. To develop a better approach to detect drug resistance, a small sample of Escherichia coli resistance data from 2003 to 2014 in Chengdu, Sichuan Province was used, and multiple regression interpolation was applied to impute missing data based on the time series. Next, cluster analysis was used to classify anti-<i>E. coli</i> drugs. According to the classification results, a GM(1,1)-BP model was selected to analyze the changes in the drug resistance of <i>E. coli</i>, and a drug resistance prediction system was constructed based on the GM(1,1)-BP Neural Network model. The GM(1,1)-BP Neural Network model showed a good prediction effect using a small sample of drug resistance data, with a determination coefficient R<sup>2</sup> of 0.7830 and an RMSE of only 0.0527. This model can be applied for the prediction of drug resistance trends of other animal-derived pathogenic bacteria, and provides the scientific and technical means for the effective assessment of bacterial resistance. |
topic |
drug resistance microbial BP neural network grey system GM(1,1)-BP neural network model |
url |
https://www.mdpi.com/2079-6382/10/6/692 |
work_keys_str_mv |
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