Author: Xiaoyang Ji; Chunming Zhang; Yubo Zhai; Zhonghai Zhang; Yiqing Xue; Chunli Zhang; Guangming Tan; Gang Niu
Title: 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 Document date: 2020_2_26
ID: f21dknmb_43
Snippet: Similar to the process of identifying CSHG, we calculated whether entities were significantly distributed in a specific corpus. We counted the number of texts containing each CSHG in a specific corpus, and then counted the number of each candidate entity in the corpus subset. Next, we randomly selected the same amount of text from the random control corpus and then counted the number of each candidate entity in this subset of the random corpus. T.....
Document: Similar to the process of identifying CSHG, we calculated whether entities were significantly distributed in a specific corpus. We counted the number of texts containing each CSHG in a specific corpus, and then counted the number of each candidate entity in the corpus subset. Next, we randomly selected the same amount of text from the random control corpus and then counted the number of each candidate entity in this subset of the random corpus. This was repeated 100-10000 times in the random corpus to generate candidate entities in the specified amount of text of the random distribution model. According to the central limit theorem (CLT), the distribution of random sampling averages of randomly distributed data always conforms to a normal distribution. Therefore, we can use the Z score to evaluate whether an entity is significant in a specific text. Here, we used a Z score cutoff value > 6.
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