在乌干达的Mycobacterium结核病临床分离物中基于机器学习的抗生素耐药性的预测
Sandra Ruth Babirye1,2, Mike Nsubuga2,3,4,5, Gerald Mboowa1,2
1Department of Immunology and Molecular Biology, School of Biomedical Sciences, College of Health Sciences, Makerere University, P.O. Box 7072, Kampala, Uganda.
BMC infectious diseases
|December 5, 2024
概括
机器学习模型可以使用基因组数据预测Mycobacterium结核病耐药性. 后勤回归,XGBoost和GBC表现出不同程度的成功,有可能改善监测和干预.
科学领域:
- 基因组医学是一种基因组医学.
- 计算生物学是一种计算生物学.
- 传染病流行病学 传染病流行病学
背景情况:
- 结核病 (TB) 的管理受到耐药性Mycobacterium tuberculosis (MTB) 的阻碍.
- 预测耐药性对于有效控制结核病至关重要.
研究的目的:
- 评估机器学习算法,用于预测MTB中对四种关键抗结核药物 (利番素,异亚酸,菌素,乙醇) 的耐药性.
- 通过乌干达和南非的基因组和临床数据来评估模型的概括性.
主要方法:
- 在MTB分离物 (n=182) 上训练了十个机器学习算法,使用基因组 (SNP突变) 和临床数据.
- 使用五倍交叉验证,选择基于马修斯相关系数 (MCC) 和ROC曲线下的面积 (AUC) 的模型.
- 在独立数据集上评估模型性能和概括性.
主要成果:
- 后勤回归在预测利法素和 estreptomycin 耐药性方面表现出色;XGBoost 对于乙醇;GBC 对于 isoniazid.
- 仅在SNP数据上训练的模型表现比包括临床变量在内的模型更好.
- 概括性有所不同,GBC和XGBoost在南非数据集上表现更好,而不是物流回归.
- 确定了关键突变和艾滋病毒状况作为药物耐药性的重要预测因素.
结论:
- 机器学习为预测MTB中的抗菌素耐药性提供了一个有希望的方法.
- 整合基因组和临床数据可以提高耐药性预测的准确性.
- 这些模型可以支持强大的监测系统,并为有针对性的结核病干预提供信息.
关键词:
结核病菌菌菌的结核病菌的结核病菌.抗微生物耐药性 抗微生物耐药性临床 临床 临床 临床药物耐药性 药物耐药性 药物耐药性基因 基因 基因 基因机器学习 机器学习突变 突变 突变 突变整个基因组序列的测序更多相关视频
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