论文标题
Vapur：在Covid-19文献中找到相关蛋白质化合对的搜索引擎
Vapur: A Search Engine to Find Related Protein-Compound Pairs in COVID-19 Literature
论文作者
论文摘要
2019年的冠状病毒病（Covid-19）在全球造成了可怕的后果，并引发了不同领域的激烈科学努力。由此产生的出版物创造了一个巨大的文本集，其中发现与感兴趣的生物分子有关的研究对于通用搜索引擎来说是挑战，因为出版物富含特定领域的术语。在这里，我们介绍Vapur：在线Covid -19搜索引擎，专门设计用于查找相关蛋白质 - 化学对。 Vapur具有以关系为导向的倒置指数，该指数能够与其相关实体进行查询生物分子进行检索和小组研究。 VAPUR的倒置索引是使用Bionlp管道自动创建的，并与在线用户界面集成在一起。在线界面设计用于域研究人员的平稳遍历当前文献，并在https://tabilab.cmpe.boun.edu.tr/vapur/上公开获得。
Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains. The resulting publications created a huge text collection in which finding the studies related to a biomolecule of interest is challenging for general purpose search engines because the publications are rich in domain specific terminology. Here, we present Vapur: an online COVID-19 search engine specifically designed to find related protein - chemical pairs. Vapur is empowered with a relation-oriented inverted index that is able to retrieve and group studies for a query biomolecule with respect to its related entities. The inverted index of Vapur is automatically created with a BioNLP pipeline and integrated with an online user interface. The online interface is designed for the smooth traversal of the current literature by domain researchers and is publicly available at https://tabilab.cmpe.boun.edu.tr/vapur/ .
