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对比各种特征提取和机器学习方法,用于预测streptococcus pneumoniae中的抗菌素耐药性

Deniz Ece Kaya¹, Ege Ülgen¹, Ayşe Sesin Kocagöz²

¹Department of Biostatistics and Medical Informatics, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Türkiye.

Frontiers in antibiotics

|January 16, 2025

概括

机器学习模型可以使用遗传数据预测Streptococcus pneumoniae中的抗菌素耐药性 (AMR). 不同的机器学习方法和特征类型显著影响像青素这样的抗生素的预测准确性.

科学领域:

基因组学和生物信息学
计算生物学计算生物学
传染性疾病传染性疾病

背景情况:

肺炎链球菌 (Streptococcus pneumoniae) 由于其高发病率,死亡率和抗菌素耐药性 (AMR) 的增加,构成了全球重大健康挑战.
全基因组测序和机器学习 (ML) 的进步为了解和预测S. pneumoniae中的AMR表型提供了新的途径.

研究的目的:

为了比较不同机器学习模型和遗传特征在S. pneumoniae中预测AMR的有效性.
评估对青素,红素和四环素耐药性的预测准确度.

主要方法:

利用来自欧洲核酸档案 (ENA) 的980个S. pneumoniae菌株的全基因组测序数据.
提取并比较遗传特征,包括核酸k-mers,氨基酸k-mers和单核酸多态 (SNP).
训练并比较各种机器学习模型:随机森林,支向量机,随机梯度增强和极端梯度增强.

主要成果:

机器学习方法的选择和使用的特定遗传特征显著影响了AMR预测的准确性.
不同的特征集 (k-mers,SNP,组合) 在测试的抗生素中产生了不同的性能水平.

结论:

机器学习方法可用于预测S. pneumoniaeAMR表型.
优化模型设置和特征选择对于提高未来抗抗药性监测和临床应用中的预测准确性和效率至关重要.

关键词:

抗菌药物反症 (AMR) 是一种在SNP中,SNP是SNP.克默尔 (Kmer) 是一个机器学习是机器学习.链球菌 pneumonaieieieie 是一个病毒.全基因组测序 (WGS) 是一种全基因组测序.

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