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监督机器学习实践的协调,以基于基因组数据有效地归因Listeria monocytogenes的来源

Pierluigi Castelli¹, Andrea De Ruvo¹, Andrea Bucciacchio¹

¹Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise "Giuseppe Caporale" (IZSAM), National Reference Centre (NRC) for Whole Genome Sequencing of microbial pathogens: data base and bioinformatics analysis (GENPAT), Via Campo Boario, Teramo, TE, 64100, Italy.

|September 22, 2023

概括

使用机器学习的基因组数据分析改善了Listeria monocytogenes来源归因. 配件基因和带有80%训练数据的pan-kmers和XGBoost模型提供最佳的预测性能.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
机器学习机器学习

背景情况:

基因组数据和机器学习 (ML) 对于实时的食品传播细菌监测至关重要.
Listeria monocytogenes来源归因是这些工具的一个关键应用.
ML实践的异质性需要绩效评估.

研究的目的:

识别影响Listeria monocytogenes源预测性能的ML实践.
评估不同基因组特征,数据分割和预处理对ML模型准确性的影响.
为了比较各种ML模型在基因组数据基础上的细菌源归因中的有效性.

主要方法:

已知来源的1100个Listeria monocytogenes基因组的数据集被编译出来.
基因组概况包括7位点等位基因,核心等位基因,辅助基因,核心SNP和泛基因.
一个工作流评估了使用多种训练分割 (50-90%),近零差异删除和ML模型 (BLR,ERT,RF,SGB,SVM,XGB) 的预测性能.

主要成果:

与核心等位基因或SNP相比,辅助基因和泛基因基因的准确性更高.
80%的培训数据分割产生了比其他比例更高的准确性.
SVM和XGBoost模型实现了最高的精度,而XGBoost在计算上更高效.

结论:

优化的ML实践,包括特征选择 (辅助基因,泛基因) 和模型选择 (XGBoost),增强了Listeria monocytogenes来源归因.
该研究提供了一个多功能,自由可用的工作流程,适用于其他微生物基因组分析任务.
提供了对基因组监测中的ML实践的建议.

关键词:

基因组数据基因组数据李斯特里亚单细胞原生菌Listeria monocytogenes 机器学习是机器学习.来源归因来源归因

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