A multi-modal data harmonisation approach for discovery of COVID-19 drug targets.

ABSTRACT: Despite the volume of experiments performed and data available, the complex biology of coronavirus SARS-COV-2 is not yet fully understood. Existing molecular profiling studies have focused on analysing functional omics data of a single type, which captures changes in a small subset of the molecular perturbations caused by the virus. As the logical next step, results from multiple such omics analysis may be aggregated to comprehensively interpret the molecular mechanisms of SARS-CoV-2. An alternative approach is to integrate data simultaneously in a parallel fashion to highlight the inter-relationships of disease-driving biomolecules, in contrast to comparing processed information from each omics level separately. We demonstrate that valuable information may be masked by using the former fr

SUBMITTER: Chen T 

PROVIDER: S-EPMC8194516 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature