MOATAI-VIR predicts disease-protein-pathway relationships for 22 clinical manifestations attributed to COVID-19’s severe adverse events. To the best of our knowledge, this is the first systematic approach to further understand the underlying etiology of COVID-19’s severe responses. More generally, MOATAI-VIR provides a clinically actionable methodology to understand the long-term consequences of viral infections.Notice:This server is freely available to all academic and non-commercial users.
Commercial users – to use this server, or request an evaluation copy, please send an email to Dr. Jeffrey Skolnick: skolnick@gatech.edu.
Citation: Astore, C, Zhou H, Jacob J, Skolnick J. 2021. Prediction of severe adverse events, modes of action and drug treatments for COVID-19’s complications. Scientific Reports.11: 20864. PDF
Press Release: AI Tool Pairs Protein Pathways with Clinical Side Effects, Patient Comorbidities to Suggest Targeted Covid-19 Treatments
Picked up by Georgia Tech Research Horizons, Mirage News, ScienMag, MedicalXpress, Newswire, and The Medical News.
Full list of FDA-approved drugs predicted for each complication using the SARS-CoV-2 interactome.
Full list of FDA-approved drugs predicted for each complication using GWAS.
Supplementary Information
Tables included in the Supplementary Information
| Table | Description |
|---|---|
| S0 | Comparison of LeMeDISCO’s J-score with the XD score, NG, SAB score and symptom similarity for correlations with comorbidity quantified by the log(RR) score, φ-score and recall. |
| S1 | Hierarchically ranked comorbidities, comorbidity enriched MOA proteins, pathways and the top 20 drugs for each COVID-19 clinical manifestation from the SARS-CoV-2 interactome as input results. |
| S2 | Hierarchically ranked comorbidities, comorbidity enriched MOA proteins, pathways and the top 20 drugs for each COVID-19 clinical manifestation from the GWAS risk genes as input results. |
| S3 | Hierarchically ranked comorbidities, comorbidity enriched MOA proteins, and the pathways for each uncharacterized COVID-19 manifestation from the SARS-CoV-2 interactome as input results. |
| S4 | Hierarchically ranked comorbidities, comorbidity enriched MOA proteins, and pathways for each uncharacterized COVID-19 manifestation from the GWAS risk genes as input results. |
| S5 | Dependence of calculated precision/enrichment factor of CoMOAdrug and CoVLS for the top 20 drugs on the cutoff of the number of known drugs associated with a disease. |
| S6 | Interactome as input predicted neoplasm comorbidity enriched MOA proteins that are labeled as cancer associated in the COSMIC database. |
| S7 | GWAS risk genes2 as input predicted neoplasm comorbidity enriched MOA proteins that are labeled as cancer associated in the COSMIC database. |
| S8 | COVID-19 differentially expressed genes from Ref. 37 ranked by their adjusted p-value that are mapped to the COSMIC database. |
| S9 | Statistical significance (p-value) of virus human interacting proteins’ overlap with the 723 COSMIC cancer drivers. |
Please send questions to Dr. Hongyi Zhou or Courtney Astore.