Abhijit Dasgupta, Abhisek Bakshi, Srijani Mukherjee, Kuntal Das, Soumyajeet Talukdar, Pratyayee Chatterjee, Sagnik Mondal, Puspita Das, Subhrojit Ghosh, Archisman Som, Pritha Roy, Rima Kundu, Akash Sarkar, Arnab Biswas, Karnelia Paul, Sujit Basak, Krishnendu Manna, Chinmay Saha, Satinath Mukhopadhyay, Nitai P Bhattacharyya, Rajat K De
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We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID-19. 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Epidemiological challenges in pandemic coronavirus disease (COVID-19): Role of artificial intelligence.
World is now experiencing a major health calamity due to the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2. The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as deep learning, in (i) rapid disease detection from x-ray or computed tomography (CT) or high-resolution CT (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) forecasting the disease and psychological impact on the population from social networking data, and (iv) prediction of drug-protein interactions for repurposing the drugs, has attracted much attention. In the present study, we describe the role of various AI-based technologies for rapid and efficient detection from CT images complementing quantitative real-time polymerase chain reaction and immunodiagnostic assays. AI-based technologies to anticipate the current pandemic pattern, prevent the spread of disease, and face mask detection are also discussed. We inspect how the virus transmits depending on different factors. We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID-19. This article is categorized under:Application Areas > Health CareAlgorithmic Development > Biological Data MiningTechnologies > Machine Learning.
期刊介绍:
The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD) are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.