%0 Journal Article %1 bowler2022machine %A Bowler, Scott %A Papoutsoglou, Georgios %A Karanikas, Aristides %A Tsamardinos, Ioannis %A Corley, Michael J. %A Ndhlovu, Lishomwa C. %D 2022 %J Scientific Reports %K DNA covid learning machine %N 1 %P 17480-- %R 10.1038/s41598-022-22201-4 %T A machine learning approach utilizing DNA methylation as an accurate classifier of COVID-19 disease severity %U https://doi.org/10.1038/s41598-022-22201-4 %V 12 %X 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.

@article{bowler2022machine, 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.}, added-at = {2023-03-07T22:52:39.000+0100}, author = {Bowler, Scott and Papoutsoglou, Georgios and Karanikas, Aristides and Tsamardinos, Ioannis and Corley, Michael J. and Ndhlovu, Lishomwa C.}, biburl = {https://www.bibsonomy.org/bibtex/224959130925e38210da9cab651bbaaaf/mensxmachina}, doi = {10.1038/s41598-022-22201-4}, interhash = {c95ccd60f041a590226ac5efad7c573c}, intrahash = {24959130925e38210da9cab651bbaaaf}, issn = {20452322}, journal = {Scientific Reports}, keywords = {DNA covid learning machine}, number = 1, pages = {17480--}, refid = {Bowler2022}, timestamp = {2023-03-07T22:52:39.000+0100}, title = {A machine learning approach utilizing DNA methylation as an accurate classifier of COVID-19 disease severity}, url = {https://doi.org/10.1038/s41598-022-22201-4}, volume = 12, year = 2022 }