國家衛生研究院 NHRI:Item 3990099045/14908

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Title:	Using random forest to predict antimicrobial minimum inhibitory concentrations of nontyphoidal Salmonella in Taiwan
Authors:	Wang, CC;Hung, YT;Chou, CY;Hsuan, SL;Chen, ZW;Chang, PY;Jan, TR;Tung, CW
Contributors:	Institute of Biotechnology and Pharmaceutical Research
Abstract:	Antimicrobial resistance (AMR) is a global health issue and surveillance of AMR can be useful for understanding AMR trends and planning intervention strategies. Salmonella, widely distributed in food-producing animals, has been considered the first priority for inclusion in the AMR surveillance program by the World Health Organization (WHO). Recent advances in rapid and affordable whole-genome sequencing (WGS) techniques lead to the emergence of WGS as a one-stop test to predict the antimicrobial susceptibility. Since the variation of sequencing and minimum inhibitory concentration (MIC) measurement methods could result in different results, this study aimed to develop WGS-based random forest models for predicting MIC values of 24 drugs using data generated from the same laboratories in Taiwan. The WGS data have been transformed as a feature vector of 10-mers for machine learning. Based on rigorous validation and independent tests, a good performance was obtained with an average mean absolute error (MAE) less than 1 for both validation and independent test. Feature selection was then applied to identify top-ranked 10-mers that can further improve the prediction performance. For surveillance purposes, the genome sequence-based machine learning methods could be utilized to monitor the difference between predicted and experimental MIC, where a large difference might be worthy of investigation on the emerging genomic determinants.
Date:	2023-02-06
Relation:	Veterinary Research. 2023 Feb 06;54(1):Article number 11.
Link to:	http://dx.doi.org/10.1186/s13567-023-01141-5
JIF/Ranking 2023:	http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=0928-4249&DestApp=IC2JCR
Cited Times(WOS):	https://www.webofscience.com/wos/woscc/full-record/WOS:000926594200001
Appears in Collections:	[Chun-Wei Tung] Periodical Articles

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