Antibacterial Activity Prediction Model of Traditional Chinese Medicine Based on Combined Data-Driven Approach and Machine Learning Algorithm: Constructed and Validated

Traditional Chinese medicines (TCMs), as a unique natural medicine resource, were used to prevent and treat bacterial diseases in China with a long history. To provide a prediction model of screening antibacterial TCMs for the design and discovery of novel antibacterial agents, the literature about...

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Autores principales:	Jin-Tong Li, Ya-Wen Wei, Meng-Yu Wang, Chun-Xiao Yan, Xia Ren, Xian-Jun Fu
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	traditional Chinese medicine (TCM) antibacterial activity distribution law machine learning model construction Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/e06f7549f1fc4aa8b99f184465495026
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spelling	oai:doaj.org-article:e06f7549f1fc4aa8b99f1844654950262021-11-22T05:20:44ZAntibacterial Activity Prediction Model of Traditional Chinese Medicine Based on Combined Data-Driven Approach and Machine Learning Algorithm: Constructed and Validated1664-302X10.3389/fmicb.2021.763498https://doaj.org/article/e06f7549f1fc4aa8b99f1844654950262021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmicb.2021.763498/fullhttps://doaj.org/toc/1664-302XTraditional Chinese medicines (TCMs), as a unique natural medicine resource, were used to prevent and treat bacterial diseases in China with a long history. To provide a prediction model of screening antibacterial TCMs for the design and discovery of novel antibacterial agents, the literature about antibacterial TCMs in the China National Knowledge Infrastructure (CNKI) and Web of Science database was retrieved. The data were extracted and standardized. A total of 28,786 pieces of data from 904 antibacterial TCMs were collected. The data of plant medicine were the most numerous. The result of association rules mining showed a high correlation between antibacterial activity with cold nature, bitter and sour tastes, hemostatic, and purging fire efficacies. Moreover, TCMs with antibacterial activity showed a specific aggregation in the phylogenetic tree; 92% of them came from Tracheophyta, of which 74% were mainly concentrated in rosids, asterids, Liliopsida, and Ranunculales. The prediction models of anti-Escherichia coli and anti-Staphylococcus aureus activity, with AUC values (the area under the ROC curve) of 77.5 and 80.0%, respectively, were constructed by the Neural Networks (NN) algorithm after Bagged Classification and Regression Tree (Bagged CART) and Linear Discriminant Analysis (LDA) selection. The in vitro experimental results showed the prediction accuracy of these two models was 75 and 60%, respectively. Four TCMs (Cirsii Japonici Herba Carbonisata, Changii Radix, Swertiae Herba, Callicarpae Formosanae Folium) were proposed for the first time to show antibacterial activity against E. coli and/or S. aureus. The results implied that the prediction model of antibacterial activity of TCMs based on properties and families showed certain prediction ability, which was of great significance to the screening of antibacterial TCMs and can be used to discover novel antibacterial agents.Jin-Tong LiJin-Tong LiYa-Wen WeiYa-Wen WeiMeng-Yu WangMeng-Yu WangChun-Xiao YanXia RenXia RenXian-Jun FuXian-Jun FuXian-Jun FuFrontiers Media S.A.articletraditional Chinese medicine (TCM)antibacterial activitydistribution lawmachine learningmodel constructionMicrobiologyQR1-502ENFrontiers in Microbiology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	traditional Chinese medicine (TCM) antibacterial activity distribution law machine learning model construction Microbiology QR1-502
spellingShingle	traditional Chinese medicine (TCM) antibacterial activity distribution law machine learning model construction Microbiology QR1-502 Jin-Tong Li Jin-Tong Li Ya-Wen Wei Ya-Wen Wei Meng-Yu Wang Meng-Yu Wang Chun-Xiao Yan Xia Ren Xia Ren Xian-Jun Fu Xian-Jun Fu Xian-Jun Fu Antibacterial Activity Prediction Model of Traditional Chinese Medicine Based on Combined Data-Driven Approach and Machine Learning Algorithm: Constructed and Validated
description	Traditional Chinese medicines (TCMs), as a unique natural medicine resource, were used to prevent and treat bacterial diseases in China with a long history. To provide a prediction model of screening antibacterial TCMs for the design and discovery of novel antibacterial agents, the literature about antibacterial TCMs in the China National Knowledge Infrastructure (CNKI) and Web of Science database was retrieved. The data were extracted and standardized. A total of 28,786 pieces of data from 904 antibacterial TCMs were collected. The data of plant medicine were the most numerous. The result of association rules mining showed a high correlation between antibacterial activity with cold nature, bitter and sour tastes, hemostatic, and purging fire efficacies. Moreover, TCMs with antibacterial activity showed a specific aggregation in the phylogenetic tree; 92% of them came from Tracheophyta, of which 74% were mainly concentrated in rosids, asterids, Liliopsida, and Ranunculales. The prediction models of anti-Escherichia coli and anti-Staphylococcus aureus activity, with AUC values (the area under the ROC curve) of 77.5 and 80.0%, respectively, were constructed by the Neural Networks (NN) algorithm after Bagged Classification and Regression Tree (Bagged CART) and Linear Discriminant Analysis (LDA) selection. The in vitro experimental results showed the prediction accuracy of these two models was 75 and 60%, respectively. Four TCMs (Cirsii Japonici Herba Carbonisata, Changii Radix, Swertiae Herba, Callicarpae Formosanae Folium) were proposed for the first time to show antibacterial activity against E. coli and/or S. aureus. The results implied that the prediction model of antibacterial activity of TCMs based on properties and families showed certain prediction ability, which was of great significance to the screening of antibacterial TCMs and can be used to discover novel antibacterial agents.
format	article
author	Jin-Tong Li Jin-Tong Li Ya-Wen Wei Ya-Wen Wei Meng-Yu Wang Meng-Yu Wang Chun-Xiao Yan Xia Ren Xia Ren Xian-Jun Fu Xian-Jun Fu Xian-Jun Fu
author_facet	Jin-Tong Li Jin-Tong Li Ya-Wen Wei Ya-Wen Wei Meng-Yu Wang Meng-Yu Wang Chun-Xiao Yan Xia Ren Xia Ren Xian-Jun Fu Xian-Jun Fu Xian-Jun Fu
author_sort	Jin-Tong Li
title	Antibacterial Activity Prediction Model of Traditional Chinese Medicine Based on Combined Data-Driven Approach and Machine Learning Algorithm: Constructed and Validated
title_short	Antibacterial Activity Prediction Model of Traditional Chinese Medicine Based on Combined Data-Driven Approach and Machine Learning Algorithm: Constructed and Validated
title_full	Antibacterial Activity Prediction Model of Traditional Chinese Medicine Based on Combined Data-Driven Approach and Machine Learning Algorithm: Constructed and Validated
title_fullStr	Antibacterial Activity Prediction Model of Traditional Chinese Medicine Based on Combined Data-Driven Approach and Machine Learning Algorithm: Constructed and Validated
title_full_unstemmed	Antibacterial Activity Prediction Model of Traditional Chinese Medicine Based on Combined Data-Driven Approach and Machine Learning Algorithm: Constructed and Validated
title_sort	antibacterial activity prediction model of traditional chinese medicine based on combined data-driven approach and machine learning algorithm: constructed and validated
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/e06f7549f1fc4aa8b99f184465495026
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Antibacterial Activity Prediction Model of Traditional Chinese Medicine Based on Combined Data-Driven Approach and Machine Learning Algorithm: Constructed and Validated

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