A machine learning approach utilizing DNA methylation as an accurate classifier of COVID-19 disease severity

Abstract

Since the onset of the COVID-19 pandemic, increasing cases with variable outcomes continue globally because of variants and despite vaccines and therapies. There is a need to identify at-risk individuals early that would benefit from timely medical interventions. DNA methylation provides an opportunity to identify an epigenetic signature of individuals at increased risk. We utilized machine learning to identify DNA methylation signatures of COVID-19 disease from data available through NCBI Gene Expression Omnibus. A training cohort of 460 individuals (164 COVID-19-infected and 296 non-infected) and an external validation dataset of 128 individuals (102 COVID-19-infected and 26 non-COVID-associated pneumonia) were reanalyzed. Data was processed using ChAMP and beta values were logit transformed. The JADBio AutoML platform was leveraged to identify a methylation signature associated with severe COVID-19 disease. We identified a random forest classification model from 4 unique methylation sites with the power to discern individuals with severe COVID-19 disease. The average area under the curve of receiver operator characteristic (AUC-ROC) of the model was 0.933 and the average area under the precision-recall curve (AUC-PRC) was 0.965. When applied to our external validation, this model produced an AUC-ROC of 0.898 and an AUC-PRC of 0.864. These results further our understanding of the utility of DNA methylation in COVID-19 disease pathology and serve as a platform to inform future COVID-19 related studies.

Epigenetic perspectives associated with COVID-19 infection and related cytokine storm: an updated review

“…Similarly, 'Epithelial Stromal Interaction 1 (EPSI1), important for macrophage differentiation, gets hypomethylated at particular CpG sequences in COVID-19 patients. Interferon Regulatory Factor 7 (IRF7), which plays an essential role in innate immunity, is less methylated at particular CpG sites of COVID-19 patients than the normal individual [101]. The DNA methylation status of other genes also influences the circumstances of COVID patients.…”

Machine learning models based on fluid immunoproteins that predict non-AIDS adverse events in people with HIV

“…Various ML-based algorithms have been used to reveal biological features in the classification of complex diseases, such as CVD and cancer, 19 , 20 and predict changes in immune function, disease severity, and mortality risk in infectious diseases. 21 , 22 , 23 Previous studies have used ML to predict comorbid and virological failure outcomes in PWH based strictly with social-demographic and clinical-related variables 24 , 25 ; however, applying an ML approach may be an effective tool to uncover composite immune features that can accurately identify individuals at the highest risk of NAEs and those who could benefit from recurrent clinical monitoring.…”

A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning

“…Automated machine learning (AutoML) is a process that allows to fully automate the machine learning process end-to-end. In particular, the JADBio platform used in this study, is an ad-hoc tool for biomedical research, with a proven capacity to effectively build predictive models by analyzing relatively small datasets, such as often those of patients [24][25][26][27][28][29][30][31]. It can automate the pre-analysis steps including data integration, preprocessing, cleaning, and engineering (feature construction), the analysis steps including algorithm selection, training of the models, and hyperparameter optimization, as well as the post-analysis steps including interpretation, explanation, and visualization of the analysis process and the output model.…”