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

@article{Napolitano2022ImpactOC,
  title={Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies},
  author={Francesco Napolitano and Xiaopeng Xu and Xin Gao},
  journal={Briefings in Bioinformatics},
  year={2022}
}
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… 
View PDF
2 Citations
Highly Influential Citations
1
Background Citations
1

Figures from this paper

2 Citations

Towards a Unified Pandemic Management Architecture: Survey, Challenges and Future Directions
TLDR
A unified pandemic management architecture is envisioned that leverages the IoT and edge computing to automate recommendations on vaccine distribution, dynamic lockdown, mobility scheduling and pandemic prediction, and elucidate the flow of data among the layers of the architecture.
2′- and 3′-Ribose Modifications of Nucleotide Analogues Establish the Structural Basis to Inhibit the Viral Replication of SARS-CoV-2
TLDR
The results suggest that 2′-modification couples with the protein environment to affect the structural stability, while 3′-hydrogen substitution inherently exerts “immediate termination” without compromising theStructural stability in the active site.

References

SHOWING 1-10 OF 208 REFERENCES
Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review
A review of modern technologies for tackling COVID-19 pandemic
Lessons from the COVID-19 pandemic for advancing computational drug repurposing strategies
TLDR
This Review examines a representative set of currently used computational approaches to identify repurposable drugs for COVID-19, as well as their underlying data resources, and proposes a strategy to make computational drug repurposing more effective in future pandemics.
Artificial intelligence in COVID-19 drug repurposing
Therapeutics for COVID-19: from computation to practices—where we are, where we are heading to
TLDR
A comprehensive review dealing with up-to-date information of therapeutics or drug regimens being utilized by physicians to treat COVID-19 patients along with in-depth discussion of mechanism of action of these drugs and their targets is presented.
Automated detection of COVID-19 cases using deep neural networks with X-ray images
COVID‐19 drug repurposing: A review of computational screening methods, clinical trials, and protein interaction assays
TLDR
This review divides the COVID‐19 drug repurposing research into three large groups, including clinical trials, computational research, and in vitro protein‐binding experiments to facilitate future drug discovery and the creation of effective drug combinations.
Computational drug discovery and repurposing for the treatment of COVID-19: A systematic review
Associations of the BNT162b2 COVID-19 vaccine effectiveness with patient age and comorbidities
TLDR
Effectiveness was uniform for men and women yet declined mildly but significantly with age and for patients with specific chronic comorbidities, most notably type 2 diabetes.
A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019.
The results of research on the use of artificial intelligence (AI) for medical imaging of the lungs of patients with coronavirus disease 2019 (COVID-19) has been published in various forms. In this…
...
...