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<p>COVID-19 disease outcomes can vary considerably among infected patients. Most studies have focused on patients with severe COVID-19. However, investigations of asymptomatic infection can provide insights into patient-specific immunological features that protect patients from COVID-19 symptoms. Recent studies have shown an association between common human leukocyte antigen (HLA) alleles and asymptomatic COVID-19 infections. Here we utilize machine learning in conjunction with explainable AI (XAI) to identify alleles in five HLA loci that can be either protective or put the patient at risk for symptomatic COVID-19. Data from the public online HLA-COVID database (1946 samples) was used for training and validating multiple ML classification models to identify the top performing model. The model was then further processed with XAI via SHAP (SHapley Additive exPlanations) to identify the protective and high-risk HLA alleles. This study provides a proof-of-concept study for utilizing machine learning to provide valuable insights for COVID-19 patients. These findings can be translated into clinical algorithms to help physicians personalize COVID-19 treatments and achieve better clinical outcomes.</p> Graphical Abstract <p></p>

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Identification of HLA Variants Associated with Symptomatic and Asymptomatic COVID-19 Using a Machine Learning Approach

  • 期刊论文

  • 发表日期：

    2026年1月26日

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  • Atul Rawal,
  • Zuben Sauna

摘要

COVID-19 disease outcomes can vary considerably among infected patients. Most studies have focused on patients with severe COVID-19. However, investigations of asymptomatic infection can provide insights into patient-specific immunological features that protect patients from COVID-19 symptoms. Recent studies have shown an association between common human leukocyte antigen (HLA) alleles and asymptomatic COVID-19 infections. Here we utilize machine learning in conjunction with explainable AI (XAI) to identify alleles in five HLA loci that can be either protective or put the patient at risk for symptomatic COVID-19. Data from the public online HLA-COVID database (1946 samples) was used for training and validating multiple ML classification models to identify the top performing model. The model was then further processed with XAI via SHAP (SHapley Additive exPlanations) to identify the protective and high-risk HLA alleles. This study provides a proof-of-concept study for utilizing machine learning to provide valuable insights for COVID-19 patients. These findings can be translated into clinical algorithms to help physicians personalize COVID-19 treatments and achieve better clinical outcomes.

Graphical Abstract