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bioRxiv · 10.1101/2022.07.01.496571

CovidOutcome2: a tool for SARS-CoV2 mutation identification and for disease severity prediction

Kalcsevszki, R.·Horvath, A.·Gyorffy, B.·Pongor, S.·Ligeti, B.
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Abstract

Our goal was to develop a platform, CovidOutcome2, capable of predicting disease severity from viral mutation profiles using automated machine learning (autoML) and deep neural networks applied to the available large corpus of sequenced SARS-CoV2 genomes. CovidOutcome2 accepts either user-submitted genomes or user defined mutation combinations as the input. The output is a predicted severity score plus a list of identified, annotated mutations and their functional effects in VCF format. The best model performance is a ROC-AUC 0.899 for the model including patient age and ROC-AUC 0.83 for the model without patient age. AvailabilityCovidOutcome is freely available online under the URL https://www.covidoutcome.bio-ml.com as well as in a standalone version https://github.com/bio-apps/covid-outcome.

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Kalcsevszki, R., Horvath, A., Gyorffy, B., Pongor, S., Ligeti, B.. 2022-07-01. CovidOutcome2: a tool for SARS-CoV2 mutation identification and for disease severity prediction. https://doi.org/10.1101/2022.07.01.496571

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