Unknown

Dataset Information

0

Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases.


ABSTRACT: Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center 'Lean European Open Survey on SARS-CoV-2-infected patients' (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.

SUBMITTER: Linden T 

PROVIDER: S-EPMC8677630 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC5963083 | biostudies-literature
| S-EPMC5652333 | biostudies-literature
| S-EPMC6798083 | biostudies-literature
| S-EPMC7543461 | biostudies-literature
2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress
| S-EPMC8102689 | biostudies-literature
| S-EPMC6226171 | biostudies-literature
| S-EPMC6881446 | biostudies-literature
| S-EPMC8096946 | biostudies-literature
| S-EPMC7478228 | biostudies-literature