Bibliographic Details
| Title: |
Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases |
| Authors: |
Thomas Linden, Frank Hanses, Daniel Domingo-Fernández, Lauren Nicole DeLong, Alpha Tom Kodamullil, Jochen Schneider, Maria J.G.T. Vehreschild, Julia Lanznaster, Maria Madeleine Ruethrich, Stefan Borgmann, Martin Hower, Kai Wille, Torsten Feldt, Siegbert Rieg, Bernd Hertenstein, Christoph Wyen, Christoph Roemmele, Jörg Janne Vehreschild, Carolin E.M. Jakob, Melanie Stecher, Maria Kuzikov, Andrea Zaliani, Holger Fröhlich |
| Source: |
Artificial Intelligence in the Life Sciences, Vol 1, Iss , Pp 100020- (2021) |
| Publisher Information: |
Elsevier, 2021. |
| Publication Year: |
2021 |
| Collection: |
LCC:Science (General) |
| Subject Terms: |
Machine learning, Explainable ai, Precision medicine, Covid19, Drug repositioning, Science (General), Q1-390 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2667-3185 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S2667318521000209; https://doaj.org/toc/2667-3185 |
| DOI: |
10.1016/j.ailsci.2021.100020 |
| Access URL: |
https://doaj.org/article/f1ebf07fad2e486282bb8146b1b0d351 |
| Accession Number: |
edsdoj.f1ebf07fad2e486282bb8146b1b0d351 |
| Database: |
Directory of Open Access Journals |
