TWIRLS, an automated topic-wise inference method based on massive literature, suggests a possible mechanism via ACE2 for the pathological changes in the human host after coronavirus infection

Xiaoyang Ji; Chunming Zhang; Yubo Zhai; Zhonghai Zhang; Yiqing Xue; Chunli Zhang; Guangming Tan; Gang Niu

Coronavirus-study specific entities and host genes As of February 21, 2020, the PubMed database included 14,878 biomedical articles on coronaviruses. We obtained text data (called local samples) from all related articles on the coronavirus that had been peer reviewed and published by human experts, which included the title, abstracts, author and affiliation information (total 3,182,687 words). The goal of the literature mining was to identify host genes and entities that are relevant to coronavirus research and to establish connections between them. An entity can refers to a word or phrase of the concept name (including related concepts, e.g., virus structure and chemical composition, source of infection, and virus type). The gene names were defined using the mammalian official gene symbols in the Hugo Gene Naming Committee (HGNC) database. We directly retrieved 667 candidate genes from the local samples. By establishing a random distribution of one of the candidate genes in a control sample, the significance of this gene appearing in the local samples can be determined when the frequency of the current gene is an outlier of the random distribution of the control samples (see Methods for details). By calculating the odds ratio, we can also further determine the specificity of the association between this gene and the local samples. In this paper, we selected an odds ratio > 6 as the threshold for this judgment, which resulted in 123 coronavirus study-specific host genes (CSHGs).