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CORACLE (COVID-19 liteRAture CompiLEr): A platform for efficient tracking and extraction of SARS-CoV-2 and COVID-19 literature, with examples from post-COVID with respiratory involvement

Authors :
Kristina Piontkovskaya
Yulian Luo
Pia Lindberg
Jing Gao
Michael Runold
Iryna Kolosenko
Chuan-Xing Li
Åsa M. Wheelock
Source :
Computational and Structural Biotechnology Journal, Vol 23, Iss , Pp 2661-2668 (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

Background: During the COVID-19 pandemic a need to process large volumes of publications emerged. As the pandemic is winding down, the clinicians encountered a novel syndrome - Post-acute Sequelae of COVID-19 (PASC) - that affects over 10 % of those who contract SARS-CoV-2 and presents a significant challenge in the medical field. The continuous influx of publications underscores a need for efficient tools for navigating the literature. Objectives: We aimed to develop an application which will allow monitoring and categorizing COVID-19-related literature through building publication networks and medical subject headings (MeSH) maps to identify key publications and networks. Methods: We introduce CORACLE (COVID-19 liteRAture CompiLEr), an innovative web application designed to analyse COVID-19-related scientific articles and to identify research trends. CORACLE features three primary interfaces: The ''Search'' interface, which displays research trends and citation links; the ''Citation Map'' interface, allowing users to create tailored citation networks from PubMed Identifiers (PMIDs) to uncover common references among selected articles; and the ''MeSH'' interface, highlighting current MeSH trends and their associations. Results: CORACLE leverages PubMed data to categorize literature on COVID-19 and PASC, aiding in the identification of relevant research publication hubs. Using lung function in PASC patients as a search example, we demonstrate how to identify and visualize the interactions between the relevant publications. Conclusion: CORACLE is an effective tool for the extraction and analysis of literature. Its functionalities, including the MeSH trends and customizable citation mapping, facilitate the discovery of emerging trends in COVID-19 and PASC research.

Details

Language :
English
ISSN :
20010370
Volume :
23
Issue :
2661-2668
Database :
Directory of Open Access Journals
Journal :
Computational and Structural Biotechnology Journal
Publication Type :
Academic Journal
Accession number :
edsdoj.65ed2c8e0bb4042aa571bfc7314b47d
Document Type :
article
Full Text :
https://doi.org/10.1016/j.csbj.2024.06.018