Computational drug repositioning using similarity constrained weight regularization matrix factorization: A case of COVID-19.

ABSTRACT: Amid the COVID-19 crisis, we put sizeable efforts to collect a high number of experimentally validated drug-virus association entries from literature by text mining and built a human drug-virus association database. To the best of our knowledge, it is the largest publicly available drug-virus database so far. Next, we develop a novel weight regularization matrix factorization approach, termed WRMF, for in silico drug repurposing by integrating three networks: the known drug-virus association network, the drug-drug chemical structure similarity network, and the virus-virus genomic sequencing similarity network. Specifically, WRMF adds a weight to each training sample for reducing the influence of negative samples (i.e. the drug-virus association is unassociated). A comparison on the curated

SUBMITTER: Xu J 

PROVIDER: S-EPMC9258716 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature