Identifying Potential Clues On Covid-19 Through Coronavirus-Related Literature Using A Data-Driven Approach With The Help Of A Text Mining-Based Software, PredictSearch

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Patatian, Angela | Benech, Philippe

Edité par CCSD ; Rajesh K. Deshmukh -

International audience. Huge amounts of scientific publications are produced daily in particular in many fields of medicine and biological science. Managing these data and deducing valuable information to identify significant clues to understand a physiological or pathological mechanism as well as to propose therapeutic solutions are urgently needed. Here we describe the use of a dedicated text mining-based software, PredictSearch (PS) to explore, through a literature survey, significant correlations between terms related to coronavirus infection. Our search highlighted some features of antiviral compounds such as chloroquine and glycyrrhizin and their impacts on apoptotic cell death, cell cycle and endocytic pathway in the course of coronavirus infection. In addition, the reported mechanisms through which the virus can avoid the interferon-induced antiviral state should pave the way to identify efficient therapies. This study demonstrates the importance of informatics tools such as PredictSearch dedicated to scientific literature survey to adapt previous knowledge to new health issues on a particular topical subject like Covid-19 pandemia.

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