利用 CoronaCentral 分析大量的冠状病毒文献。
Analyzing the vast coronavirus literature with CoronaCentral.
机构信息
Department of Bioengineering, Stanford University, Stanford, CA 94305
Department of Bioengineering, Stanford University, Stanford, CA 94305.
出版信息
Proc Natl Acad Sci U S A. 2021 Jun 8;118(23). doi: 10.1073/pnas.2100766118.
The SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming publication rate means that researchers are unable to keep abreast of the literature. To ameliorate this, we present the CoronaCentral resource that uses machine learning to process the research literature on SARS-CoV-2 together with SARS-CoV and MERS-CoV. We categorize the literature into useful topics and article types and enable analysis of the contents, pace, and emphasis of research during the crisis with integration of Altmetric data. These topics include therapeutics, disease forecasting, as well as growing areas such as "long COVID" and studies of inequality. This resource, available at https://coronacentral.ai, is updated daily.
SARS-CoV-2 大流行促使人们对该病毒及其对人类健康影响的各个方面展开了大量研究。巨大的出版量意味着研究人员无法及时掌握文献信息。为了解决这个问题，我们开发了 CoronaCentral 资源，该资源利用机器学习来处理有关 SARS-CoV-2、SARS-CoV 和 MERS-CoV 的研究文献。我们将文献分类为有用的主题和文章类型，并通过整合 Altmetric 数据，能够分析危机期间研究的内容、速度和重点。这些主题包括治疗方法、疾病预测，以及“长新冠”和不平等研究等新兴领域。该资源可在 https://coronacentral.ai 上获取，每天更新。
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