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

Biological entity identification is a key step in the literature mining process [7, 8] . To ensure functionality of the extracted entity, we first compared the entity from the subject dictionary with the human official gene symbols in the Hugo Gene Nomenclature Commission (HGNC) database [9] to generate subject candidate genes using standard nomenclature. In addition, the entities in the abstract were capitalized to avoid errors in the identification process. To obtain widely used gene entities that are precisely related to the subject and to determine the significance of the gene distribution in the specific texts, we calculated the difference in the distribution proportions. We searched for the subject candidate genes in the subject dictionary and the randomized control dictionary, respectively. We also counted the number of abstracts containing each subject candidate gene in each abstract set, respectively. Finally, we calculated the odds ratio of each subject candidate gene and sorted them into a list of precisely related genes (CSHG).