Evaluation of Machine Learning Models for Predicting Antimicrobial Resistance of Actinobacillus pleuropneumoniae From Whole Genome Sequences-Reference-Cited by-同舟云学术

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

Published:2020-02-06 Issue: Volume:11 Page:
ISSN:1664-302X
Container-title:Frontiers in Microbiology
language:
Short-container-title:Front. Microbiol.

Author:

Liu Zhichang,Deng Dun,Lu Huijie,Sun Jian,Lv Luchao,Li Shuhong,Peng Guanghui,Ma Xianyong,Li Jiazhou,Li Zhenming,Rong Ting,Wang Gang

Publisher

Frontiers Media SA

Subject

Microbiology (medical),Microbiology

Reference18 articles.

1. Whole genome sequencing for surveillance of antimicrobial resistance in Actinobacillus pleuropneumoniae.;Bossé;Front. Microbiol.,2017

2. Algorithms for the set covering problem.;Caprara;Ann. Oper. Res.,2000

3. Benchmarking of methods for identification of antimicrobial resistance genes in bacterial whole genome data.;Clausen;J. Antimicrob. Chemother.,2016

4. Prediction of protein-protein interactions between viruses and human by an SVM model.;Cui;BMC Bioinform.,2012

5. Phenetic comparison of prokaryotic genomes using k-mers.;Déraspe;Mol. Biol. Evol.,2017

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