Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies

Francesco Napolitano, Xiaopeng Xu, Xin Gao

Research output: Contribution to journalArticlepeer-review

Abstract

SARS-CoV-2 caused the first severe pandemic of the digital era. Computational approaches have been ubiquitously used in an attempt to timely and effectively cope with the resulting global health crisis. In order to extensively assess such contribution, we collected, categorized and prioritized over 17 000 COVID-19-related research articles including both peer-reviewed and preprint publications that make a relevant use of computational approaches. Using machine learning methods, we identified six broad application areas i.e. Molecular Pharmacology and Biomarkers, Molecular Virology, Epidemiology, Healthcare, Clinical Medicine and Clinical Imaging. We then used our prioritization model as a guidance through an extensive, systematic review of the most relevant studies. We believe that the remarkable contribution provided by computational applications during the ongoing pandemic motivates additional efforts toward their further development and adoption, with the aim of enhancing preparedness and critical response for current and future emergencies.
Original languageEnglish (US)
JournalBriefings in Bioinformatics
DOIs
StatePublished - Nov 11 2021

ASJC Scopus subject areas

  • Molecular Biology
  • Information Systems

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