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用特征加权模型来解决来自Mycobacterium结核病基因组序列的药物耐药性预测中的血统依赖性

Nina Billows^1,2, Jody E Phelan³, Dong Xia¹

¹Department of Comparative Biomedical Sciences, Royal Veterinary College, London, United Kingdom.

Bioinformatics (Oxford, England)

|July 10, 2023

概括

机器学习模型可以预测结核病 (TB) 的耐药性,但可能会受到Mycobacterium结核复合体 (MTBC) 人口结构的偏差. 特征选择和权衡方法减少了这种偏差,改善了结核病耐药性预测的模型通用性.

科学领域:

基因组学就是基因组学.
机器学习机器学习
计算生物学计算生物学

背景情况:

结核病 (TB) 是由Mycobacterium结核复合体 (MTBC) 引起的,该复合体表现出克隆群体结构.
在MTBC中耐药性是对结核病治疗和根除努力的重大威胁.
机器学习 (ML) 模型越来越多地用于从整个基因组序列预测药物耐药性.

研究的目的:

调查MTBC群体结构对基于ML的耐药性预测的影响.
评估结核病耐药性的ML模型中减少血统依赖的方法.

主要方法:

在随机森林 (RF) 模型中比较了三种方法来减少血统依赖:分层,特征选择和特征权重.
使用ROC曲线下的面积 (AUC) 评估模型性能.

主要成果:

所有的射频模型都表现出中等到高性能 (AUC范围:0.60-0.98).
预测性能在一线与二线药物之间有所不同,并受到训练数据集谱系的影响.
特征权重和选择方法成功地减少了基系依赖,性能与未加权模型相比.
谱系特异型模型显示出比全球模型更高的灵敏度,可能是由于菌株特异性突变或采样.

结论:

人口结构混了MTBC药物耐药性的ML预测.
特性选择和权重是有效的策略,以减轻ML模型中的谱系依赖.
这些方法提高了ML模型的通用性,用于预测不同MTBC群体的结核病耐药性.

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