Selected article for: "candidate csse and random distribution"
Author: Xiaoyang Ji; Chunming Zhang; Yubo Zhai; Zhonghai Zhang; Chunli Zhang; Yiqing Xue; 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_3_2
    • ID: eaglecq7_8
    Snippet: For the candidate CSSE dictionary, a random distribution model for each entity was built by TWIRLS using the control samples. We identified 623 CSSEs (Table Sl, Sheet 1) based on the outliers discriminated by the random model and calculated odds ratio. For example, TWIRLS found 100 CSSEs close to ACE2, the receptor of SARS and SARS-CoV-2 viruses (see left panel in Figure 1 ). The size of the entity represents the relative distance to ACE2, with a.....
    Document: For the candidate CSSE dictionary, a random distribution model for each entity was built by TWIRLS using the control samples. We identified 623 CSSEs (Table Sl, Sheet 1) based on the outliers discriminated by the random model and calculated odds ratio. For example, TWIRLS found 100 CSSEs close to ACE2, the receptor of SARS and SARS-CoV-2 viruses (see left panel in Figure 1 ). The size of the entity represents the relative distance to ACE2, with a larger size indicating a closer distance to ACE2. Additionally, we present the CSSE cloud of the human receptor gene DPP4 of the MERS virus (see right panel in Figure 1 ).

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