使用机器学习方法识别与症状和无症状COVID-19相关的HLA变体
1Center for Biologics Evaluation & Research, Food and Drug Administration, Silver Spring, Maryland, USA.
The AAPS journal
|January 26, 2026
概括
机器学习确定了与无症状COVID-19相关的特定人类白细胞抗原 (HLA) 基因. 这项研究有助于预测患者患有症状疾病的风险,有助于个性化治疗策略.
科学领域:
- 免疫遗传学 免疫遗传学
- 计算生物学 计算生物学
- 传染病流行病学 传染病流行病学
背景情况:
- COVID-19的结果有很大差异,大多数研究都集中在严重病例上.
- 无症状感染为保护性免疫机制提供了关键的见解.
- 之前的研究表明,人类白细胞抗原 (HLA) 基因和无症状的COVID-19之间存在联系.
研究的目的:
- 采用机器学习 (ML) 和可解释AI (XAI) 来识别与COVID-19症状发展相关的HLA基因.
- 区分具有保护作用的HLA基因和增加患有COVID-19症状的风险的基因.
- 为了解 COVID-19 患者特异性免疫反应而使用 ML 建立概念验证.
主要方法:
- 使用公共数据集 (HLA-COVID数据库,1946个样本) 进行模型培训和验证.
- 开发和优化了多个ML分类模型,以根据HLA等位基来预测COVID-19症状状态.
- 应用SHAP (夏普利添加式扩展) 对XAI来解释模型预测并识别关键的HLA等位基因.
主要成果:
- 在五个位置确定了特定的HLA基因,这些基因与保护或增加患有COVID-19症状的风险有关.
- 性能最好的ML模型证明了区分保护性和高风险HLA等位基因的能力.
- SHAP分析提供了明确的解释,解释了个别HLA等位基因对疾病表现的贡献.
结论:
- 机器学习和XAI可以有效地识别影响COVID-19症状发展的HLA基因.
- 这些发现支持为个性化COVID-19风险评估开发临床算法.
- 基于HLA资料的个性化治疗策略可以改善COVID-19患者的临床结果.
关键词:
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