基于SARS-CoV-2基因组多样性的COVID-19严重程度的增强可预测性和解释性:一项涵盖四年数据的综合研究
Miao Miao1, Yonghong Ma1, Jiao Tan1
1School of Public Health, Xi'an Medical University, Xi'an, 710021, Shaanxi, China.
Scientific reports
|November 6, 2024
概括
这项研究表明,严重急性呼吸系统综合征冠状病毒2 (SARS-CoV-2) 基因组突变,以及患者年龄和疫苗接种状况等因素,显著影响2019年冠状病毒疾病 (COVID-19) 严重程度. 机器学习模型准确地预测了严重的COVID-19结果.
科学领域:
- 基因组流行病学和传染病建模.
- 计算生物学和机器学习在医疗保健中的应用.
背景情况:
- 了解2019年新冠肺炎疾病 (COVID-19) 严重程度的风险因素仍然至关重要.
- 严重急性呼吸系统综合征冠状病毒2 (SARS-CoV-2) 的基因组多样性是影响疾病结果的关键因素.
- 需要预测模型来识别严重COVID-19的高风险患者.
研究的目的:
- 调查SARS-CoV-2基因组特征,患者人口统计和疫苗接种状态对COVID-19严重程度的影响.
- 开发和验证强大的机器学习模型,用于预测严重的COVID-19.
- 为了确定与COVID-19严重程度增加相关的特定基因组标记物.
主要方法:
- 利用了来自GISAID的SARS-CoV-2基因组序列和患者信息的大数据集 (n > 19,000).
- 采用四个机器学习算法来构建预测模型,专注于基因组特征和患者数据.
- 应用沙普利添加剂exPlanes (SHAP) 用于模型解释性和关键风险因素的识别.
主要成果:
- 一个整体机器学习模型实现了高预测性能,F分数为88.84%和AUC为0.956.
- 在SARS-CoV-2基因组中确定了40多个特定的氨基酸位突变,与COVID-19严重程度有显著的关联.
- 患者的年龄,性别和疫苗接种情况被证实是严重疾病的重要预测因素.
结论:
- SARS-CoV-2 的基因组变异在确定COVID-19的严重程度方面发挥着重要作用,独立于宿主因素.
- 开发的机器学习模型为早期识别患有严重COVID-19高风险的患者提供了一个有希望的工具.
- 这些发现可以指导临床管理策略,以减少严重病例和死亡率.
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