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本文引用的文献

1
Machine learning combined with MALDI-TOF MS has the potential ability to identify serotypes of the avian pathogen Riemerella anatipestifer.机器学习与 MALDI-TOF MS 的结合具有鉴定禽流感病原体安卡拉沙门氏菌血清型的潜在能力。
J Appl Microbiol. 2023 Feb 16;134(2). doi: 10.1093/jambio/lxac075.
2
Rapid identification of methicillin-resistant Staphylococcus aureus by MALDI-TOF MS: A meta-analysis.MALDI-TOF MS 快速鉴定耐甲氧西林金黄色葡萄球菌:一项荟萃分析。
Biotechnol Appl Biochem. 2023 Jun;70(3):1217-1229. doi: 10.1002/bab.2433. Epub 2023 Jan 1.
3
Carbapenemase Producing (KPC): What Is the Best MALDI-TOF MS Detection Method.产碳青霉烯酶(KPC):最佳基质辅助激光解吸电离飞行时间质谱检测方法是什么。
Antibiotics (Basel). 2021 Dec 17;10(12):1549. doi: 10.3390/antibiotics10121549.
4
Whole-Genome Sequencing Evaluation of MALDI-TOF MS as a Species Identification Tool for Streptococcus suis.基质辅助激光解吸电离飞行时间质谱技术(MALDI-TOF MS)作为猪链球菌种鉴定工具的全基因组测序评估。
J Clin Microbiol. 2021 Oct 19;59(11):e0129721. doi: 10.1128/JCM.01297-21. Epub 2021 Sep 1.
5
Genome-wide association study identifies the virulence-associated marker in Streptococcus suis serotype 2.全基因组关联研究确定了猪链球菌2型的毒力相关标志物。
Infect Genet Evol. 2021 Aug;92:104894. doi: 10.1016/j.meegid.2021.104894. Epub 2021 May 6.
6
Streptococcus suis serotyping by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.应用基质辅助激光解吸电离飞行时间质谱技术进行猪链球菌血清型鉴定。
PLoS One. 2021 May 4;16(5):e0249682. doi: 10.1371/journal.pone.0249682. eCollection 2021.
7
MALDI-TOF mass spectrometry for sub-typing of Streptococcus pneumoniae.基质辅助激光解吸电离飞行时间质谱技术在肺炎链球菌分型中的应用。
BMC Microbiol. 2020 Dec 1;20(1):367. doi: 10.1186/s12866-020-02052-7.
8
Pan-genome analysis of Streptococcus suis serotype 2 revealed genomic diversity among strains of different virulence.猪链球菌 2 型的泛基因组分析揭示了不同毒力菌株的基因组多样性。
Transbound Emerg Dis. 2021 Mar;68(2):637-647. doi: 10.1111/tbed.13725. Epub 2020 Jul 23.
9
Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review.基于 MALDI-TOF 质谱的微生物鉴定和药敏试验的机器学习:系统评价。
Clin Microbiol Infect. 2020 Oct;26(10):1310-1317. doi: 10.1016/j.cmi.2020.03.014. Epub 2020 Mar 23.
10
Rapid classification of group B Streptococcus serotypes based on matrix-assisted laser desorption ionization-time of flight mass spectrometry and machine learning techniques.基于基质辅助激光解吸电离飞行时间质谱和机器学习技术的 B 群链球菌血清型快速分类。
BMC Bioinformatics. 2019 Dec 24;20(Suppl 19):703. doi: 10.1186/s12859-019-3282-7.

机器学习辅助 MALDI-TOF MS 作为血清型 2 及其毒力有效检测工具的前景广阔。

Promising potential of machine learning-assisted MALDI-TOF MS as an effective detector for serotype 2 and virulence thereof.

机构信息

College of Veterinary Medicine, Nanjing Agricultural University , Nanjing, China.

OIE Reference Lab for Swine Streptococcosis , Nanjing, China.

出版信息

Appl Environ Microbiol. 2023 Nov 29;89(11):e0128423. doi: 10.1128/aem.01284-23. Epub 2023 Oct 20.

DOI:10.1128/aem.01284-23
PMID:37861326
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10686076/
Abstract

To the best of our knowledge, this study reveals a strong correlation between mass spectra pattern and virulence phenotype among for the first time. In order to make the findings applicable and to excavate the intrinsic information in the spectra, the classifiers based on the machine learning algorithms were established, and RF (Random Forest)-based models have achieved an accuracy of over 90%. Overall, this study will pave the way for virulent SS2 ( serotype 2) rapid detection, and the important findings on the association between genotype and mass spectrum may provide a new idea for the genotype-dependent detection of specific pathogens.

摘要

据我们所知,这项研究首次揭示了在 之间,质荷比图谱模式与毒力表型之间存在很强的相关性。为了使研究结果具有实际应用价值并挖掘谱图中的内在信息,我们建立了基于机器学习算法的分类器,其中基于随机森林(RF)的模型的准确率超过 90%。总的来说,这项研究将为 SS2(血清型 2)的快速检测铺平道路,而关于基因型与质荷比之间关联的重要发现可能为基于基因型的特定病原体检测提供新的思路。