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

The dataset used in this pipeline were from PubMed articles. First, PubMed was searched for articles including titles, abstracts, author and affiliation information containing the subject keyword "coronavirus". The search results were downloaded in txt format for compiling into structured information. The text in the subject abstract set was organized and cleaned, and then assigned to specific corpuses related to coronavirus (specific corpus) and compiled into the subject dictionary. To enhance the accuracy of the effective entities associated with the keyword, we used a random corpus for comparisons. We searched for article abstracts containing the keyword "public health" and compiled the abstract set into a random corpus, and then compiled them into a randomized control dictionary, which contains a wide range of proteins, genes, and related biological entities. We also considered a balanced amount of information by setting relevant parameters to adjust the amount of text before carrying out the statistical analyses.