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<p>In this study, we aimed to explore the application of Artificial Intelligence (AI) in the integrated analysis of omics data to elucidate the response to SARS-CoV-2, the virus responsible for COVID-19. The pandemic has presented unprecedented challenges, necessitating the rapid development of diagnostic and therapeutic approaches. The diverse clinical manifestations of COVID-19, ranging from asymptomatic cases to severe disease, highlight the need for a deep understanding of the underlying molecular mechanisms and associated risk factors. Through a scoping review, we identified and analyzed relevant studies that applied AI to integrate omics data from basic, translational, and clinical research. Searches were conducted in the PubMed, BVS, Scopus and Web of Science databases, following a pre-defined Boolean strategy, and identified 366 records; after screening according to PRISMA, 48 studies were included. Our approach aimed to uncover complex patterns, identify diagnostic, predictive, and prognostic biomarkers, and facilitate the discovery of effective treatments. Omics data, with its vast and complex nature, poses significant challenges, including the need for data harmonization and the limitations of traditional statistical analyses. However, AI algorithms have proven to be powerful tools, capable of processing and interpreting large datasets, thereby driving innovation. AI has shown efficiency in classifying COVID-19 cases by severity through radiographic image analyses and in identifying omics biomarkers for patient stratification. These advances indicate potential improvements in clinical outcomes and personalized disease management. The integration of AI with omics analyses represents a promising frontier in COVID-19 research, offering new insights into disease mechanisms. This study underscores the importance of multidisciplinary collaboration and technological innovation in combating the pandemic and preparing for future health challenges.</p>

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Leveraging artificial intelligence for integrative omics analysis to elucidate Covid-19 response

  • 期刊论文

  • 发表日期：

    2025年11月11日

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  • Aléxia Stefani Siqueira Zetum,
  • Antônio Victor Prinz Moraes,
  • Arthur Gonzalez Brioschi,
  • Carlos Henrique Pagani Corrêa,
  • Fabio Victor Vieira Rocha,
  • Juliana Ribeiro de Oliveira,
  • Renan Pazini Borges,
  • Thais Luma de Oliveira Roza,
  • Matheus Correia Casotti,
  • Daniel Cruz Cavalieri,
  • Vinicius do Prado Ventorim,
  • Danielle Ribeiro Campos da Silva,
  • Giulia Maria Gianciti,
  • Lorena Souza Castro Altoé,
  • Iúri Drumond Louro,
  • Débora Dummer Meira

摘要

In this study, we aimed to explore the application of Artificial Intelligence (AI) in the integrated analysis of omics data to elucidate the response to SARS-CoV-2, the virus responsible for COVID-19. The pandemic has presented unprecedented challenges, necessitating the rapid development of diagnostic and therapeutic approaches. The diverse clinical manifestations of COVID-19, ranging from asymptomatic cases to severe disease, highlight the need for a deep understanding of the underlying molecular mechanisms and associated risk factors. Through a scoping review, we identified and analyzed relevant studies that applied AI to integrate omics data from basic, translational, and clinical research. Searches were conducted in the PubMed, BVS, Scopus and Web of Science databases, following a pre-defined Boolean strategy, and identified 366 records; after screening according to PRISMA, 48 studies were included. Our approach aimed to uncover complex patterns, identify diagnostic, predictive, and prognostic biomarkers, and facilitate the discovery of effective treatments. Omics data, with its vast and complex nature, poses significant challenges, including the need for data harmonization and the limitations of traditional statistical analyses. However, AI algorithms have proven to be powerful tools, capable of processing and interpreting large datasets, thereby driving innovation. AI has shown efficiency in classifying COVID-19 cases by severity through radiographic image analyses and in identifying omics biomarkers for patient stratification. These advances indicate potential improvements in clinical outcomes and personalized disease management. The integration of AI with omics analyses represents a promising frontier in COVID-19 research, offering new insights into disease mechanisms. This study underscores the importance of multidisciplinary collaboration and technological innovation in combating the pandemic and preparing for future health challenges.