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10.1093/bib/bbab456 http://scihub22266oqcxt.onion/10.1093/bib/bbab456 34788381!8689952!34788381     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Brief+Bioinform 2022 ; 23 (1): ä Nephropedia Template TP {{tp|p=34788381|t=2022. Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies |pdf=|usr=}}{{34788381}} gab.com Text Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies JO: Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34788381 #FOAvip 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. Twit Text FOAVip Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies ????Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34788381 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34788381 #FOAvip English Wikipedia <ref>{{Cite pmid|34788381}}</ref> Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies #MMPMID34788381 Napolitano F; Xu X; Gao X Brief Bioinform 2022[Jan]; 23 (1): ä PMID34788381show ga 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. |*COVID-19/epidemiology/genetics/metabolism/therapy[MESH] |*Global Health[MESH] |*Machine Learning[MESH] |*SARS-CoV-2/genetics/metabolism[MESH] |Humans[MESH]Impact of computational approaches in the fight against COVID-19: an A(epmc).pdf DeepDyve Pubget Overpricing