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

Here, we present an automated topic-wise inference method called TWIRLS (Topic-wise inference engine of massive biomedical literatures) for processing the massive biomedical literature to summarize coronavirus host-related entities. TWIRLS is capable of collecting, classifying, and analyzing reported coronavirus studies to reveal these entities based on the distribution of specific genes in the text of the articles. By combining with general protein interaction data, links between certain functional cellular/physiological components can be inferred to fill the knowledge gaps on the probable mechanism of host pathological changes. eventually leads to acute lung injury in the host. Therefore, TWIRLS can be used to guide human researchers by providing further potential therapeutic target information for the treatment of acute viral lung injury based on the regulation of RAS.