论文标题
COVID-19的信息挖掘来自大量科学文献的研究
Information Mining for COVID-19 Research From a Large Volume of Scientific Literature
论文作者
论文摘要
2020年，由于180个国家 /地区的冠状病毒菌株爆发爆发，这是史无前例的Covid-19-19大流行。为了为Covid-19的新药和疫苗拼图，许多科学家全天候工作。他们的宝贵时间和精力可能会受益于大量健康科学文献的基于计算机的挖掘，这是一群信息。在本文中，我们使用10,683个科学文章的摘要开发了一个基于图的模型，以查找有关三个主题的关键信息：传播，药物类型和与冠状病毒有关的基因组研究。为这三个主题中的每个主题构建了一个子图，以提取更多以主题为中心的信息。在每个子图中，我们都会使用中间度测量值来排序与药物，疾病，病原体，病原体和生物分子相关的关键字的重要性。结果揭示了有关抗病毒药物（氯喹，阿甘丁氨酸，地塞米松），病原体宿主（猪，蝙蝠，猕猴，氰基）的有趣信息糖蛋白）与冠状病毒的核心主题有关。这些关键字和主题的分类摘要可能是加快企业的有用参考，并为COVID-19研究推荐新的和替代方向。
The year 2020 has seen an unprecedented COVID-19 pandemic due to the outbreak of a novel strain of coronavirus in 180 countries. In a desperate effort to discover new drugs and vaccines for COVID-19, many scientists are working around the clock. Their valuable time and effort may benefit from computer-based mining of a large volume of health science literature that is a treasure trove of information. In this paper, we have developed a graph-based model using abstracts of 10,683 scientific articles to find key information on three topics: transmission, drug types, and genome research related to coronavirus. A subgraph is built for each of the three topics to extract more topic-focused information. Within each subgraph, we use a betweenness centrality measurement to rank order the importance of keywords related to drugs, diseases, pathogens, hosts of pathogens, and biomolecules. The results reveal intriguing information about antiviral drugs (Chloroquine, Amantadine, Dexamethasone), pathogen-hosts (pigs, bats, macaque, cynomolgus), viral pathogens (zika, dengue, malaria, and several viruses in the coronaviridae virus family), and proteins and therapeutic mechanisms (oligonucleotide, interferon, glycoprotein) in connection with the core topic of coronavirus. The categorical summary of these keywords and topics may be a useful reference to expedite and recommend new and alternative directions for COVID-19 research.
