WLLP: A weighted reconstruction-based linear label propagation algorithm for predicting potential therapeutic agents for COVID-19 - Chen, Langcheng; Lin, Dongying; Xu, Haojie; Li, Jianming; Lin, Lieqing (Frontiers in Microbiology, 2022)
A global epidemic caused by a new coronavirus called COVID-19 has caused a huge health and economic crisis. However, the research involved for drugs and vaccines is too expensive in terms of labor, money and time. Thanks to the development of electronic data, drug repurposing technology has become one of the effective treat-ment options available. Based on the human drug-virus association database (HDVD), we propose a model called WLLP to predict possible drug regimens. For the drug-virus association matrix, we use WKNKN method for preprocessing and label propagation of the network based on the linear neighborhood similarity of drugs and viruses, respectively, to obtain the final prediction results. In the frame-work of 10 times 10-fold-CV AUC, WLLP achieved excellent performance with AUC of 0.8828 ± 0.0037 and AUPR of 0.5277 ± 0.0053, respectively, outperform-ing the other four proposed models. We also predicted effective drug regimens for SARS-CoV-2, and this case study suggests that WLLP can be used to predict poten-tial drugs for the treatment of COVID-19.