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COVID19 药物库:从文献中挖掘文本以寻找潜在的 COVID19 疗法。

COVID19 Drug Repository: text-mining the literature in search of putative COVID19 therapeutics.

机构信息

Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, Azrieli Faculty of Medicine, Bar-Ilan University, Henrietta Szold 8, Safed 13195, Israel.

出版信息

Nucleic Acids Res. 2021 Jan 8;49(D1):D1113-D1121. doi: 10.1093/nar/gkaa969.

Abstract

The recent outbreak of COVID-19 has generated an enormous amount of Big Data. To date, the COVID-19 Open Research Dataset (CORD-19), lists ∼130,000 articles from the WHO COVID-19 database, PubMed Central, medRxiv, and bioRxiv, as collected by Semantic Scholar. According to LitCovid (11 August 2020), ∼40,300 COVID19-related articles are currently listed in PubMed. It has been shown in clinical settings that the analysis of past research results and the mining of available data can provide novel opportunities for the successful application of currently approved therapeutics and their combinations for the treatment of conditions caused by a novel SARS-CoV-2 infection. As such, effective responses to the pandemic require the development of efficient applications, methods and algorithms for data navigation, text-mining, clustering, classification, analysis, and reasoning. Thus, our COVID19 Drug Repository represents a modular platform for drug data navigation and analysis, with an emphasis on COVID-19-related information currently being reported. The COVID19 Drug Repository enables users to focus on different levels of complexity, starting from general information about (FDA-) approved drugs, PubMed references, clinical trials, recipes as well as the descriptions of molecular mechanisms of drugs' action. Our COVID19 drug repository provide a most updated world-wide collection of drugs that has been repurposed for COVID19 treatments around the world.

摘要

最近爆发的 COVID-19 疫情产生了大量大数据。迄今为止,COVID-19 开放研究数据集 (CORD-19) 列出了来自世界卫生组织 COVID-19 数据库、PubMed Central、medRxiv 和 bioRxiv 的约 130,000 篇文章,这些文章是由 Semantic Scholar 收集的。根据 LitCovid(2020 年 8 月 11 日)的数据,目前在 PubMed 中列出了约 40,300 篇与 COVID19 相关的文章。临床研究表明,分析过去的研究结果和挖掘现有数据可为成功应用目前批准的治疗方法及其组合治疗由新型 SARS-CoV-2 感染引起的疾病提供新的机会。因此,有效应对大流行需要开发用于数据导航、文本挖掘、聚类、分类、分析和推理的高效应用、方法和算法。因此,我们的 COVID19 药物库代表了一个用于药物数据导航和分析的模块化平台,重点是当前报告的与 COVID-19 相关的信息。COVID19 药物库使用户能够专注于不同复杂程度的信息,从(FDA)批准药物的一般信息、PubMed 参考文献、临床试验、配方以及药物作用的分子机制描述开始。我们的 COVID19 药物库提供了一个最新的全球药物集合,这些药物已被重新用于世界各地治疗 COVID19。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83a8/7778969/f527d9b7c2b4/gkaa969fig1.jpg

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