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Evaluation of Machine Learning Models for Predicting Antimicrobial Resistance of Actinobacillus pleuropneumoniae From Whole Genome Sequences

文献类型： 外文期刊

作者： Liu, Zhichang 1 ; Deng, Dun 1 ; Lu, Huijie 1 ; Sun, Jian 5 ; Lv, Luchao 5 ; Li, Shuhong 1 ; Peng, Guanghui 1 ; Ma, Xianyo 1 ;

作者机构： 1.Guangdong Acad Agr Sci, Inst Anim Sci, Guangzhou, Peoples R China

2.State Key Lab Livestock & Poultry Breeding, Guangzhou, Peoples R China

3.Minist Agr South China, Key Lab Anim Nutr & Feed Sci, Guangzhou, Peoples R China

4.Guangdong Engn Technol Res Ctr Anim Meat Qual & S, Guangzhou, Peoples R China

5.South China Agr Univ, Coll Vet Med, Natl Vet Microbiol Drug Resistance Risk Assessmen, Guangzhou, Peoples R China

关键词： machine learning; Support Vector Machine; Set Covering Machine; antimicrobial resistance; Actinobacillus pleuropneumoniae; genomics

期刊名称：FRONTIERS IN MICROBIOLOGY （ 2020影响因子：5.64； 五年影响因子：6.32 ）

ISSN： 1664-302X

年卷期： 2020 年 11 卷

页码：

收录情况： SCI

摘要： Antimicrobial resistance (AMR) is becoming a huge problem in countries all over the world, and new approaches to identifying strains resistant or susceptible to certain antibiotics are essential in fighting against antibiotic-resistant pathogens. Genotype-based machine learning methods showed great promise as a diagnostic tool, due to the increasing availability of genomic datasets and AST phenotypes. In this article, Support Vector Machine (SVM) and Set Covering Machine (SCM) models were used to learn and predict the resistance of the five drugs (Tetracycline, Ampicillin, Sulfisoxazole, Trimethoprim, and Enrofloxacin). The SVM model used the number of co-occurring k-mers between the genome of the isolates and the reference genes to learn and predict the phenotypes of the bacteria to a specific antimicrobial, while the SCM model uses a greedy approach to construct conjunction or disjunction of Boolean functions to find the most concise set of k-mers that allows for accurate prediction of the phenotype. Five-fold cross-validation was performed on the training set of the SVM and SCM model to select the best hyperparameter values to avoid model overfitting. The training accuracy (mean cross-validation score) and the testing accuracy of SVM and SCM models of five drugs were above 90% regardless of the resistant mechanism of which were acquired resistant or point mutation in the chromosome. The results of correlation between the phenotype and the model predictions of the five drugs indicated that both SVM and SCM models could significantly classify the resistant isolates from the sensitive isolates of the bacteria (p < 0.01), and would be used as potential tools in antimicrobial resistance surveillance and clinical diagnosis in veterinary medicine.

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