Data science research has found five key genes and seven subsypes of the new crown

Recently, researchers analyzed systematic gene interactions between two sets of gene expression data based on plasma and white blood cell sampling, published in Cell-System, and found that five key genes not only completely determine whether the sample is infected with the new coronavirus, but also can further divide the confirmed patients into seven different new coronary subtypes based on the characteristics of the infection.
these gene expression data included 19,472 genes from 100 newly crowned patients and 26 volunteers without new coronary symptoms. On February 3rd, the paper appeared in Journal of Data Science. The researchers say the findings promise to improve the accuracy of new coronary infection detection to 100 percent and to develop personalized treatments based on infection characteristics. At present, other new coronavirus studies based on gene expression data have published the highest accuracy of about 91.27 percent.Zhang Zhengjun, the only author of the
paper and a professor in the Department of Statistics at the University of Wisconsin, told China Science: "There is no literature (including new crown genes, cancer genes, AIDS genes, leukemia gene research, etc.) to accurately screen several key genes from tens of thousands of genes. The new crown gene will help develop precise new crown detectors, more efficient second-generation vaccines, and effective anti-new coronavirus drugs and therapies.
the study introduced the competitive factor effect, and based on the breadth of cognitive science, five new key genes (ABCB6, KIAA1614, MND1, SMG1, RIPK3) were identified based on the nonlinear interaction of genes with the help of a new machine learning model. These genes contain important basic information about neo-crown pneumonia.
, for example, SMG1 belongs to an unsealed mRNA-degraded gene. Unsymed mRNA degradation is a highly conservative mRNA quality monitoring system that is widely used in the endocyst cells to avoid the production and accumulation of abnormal proteins, so SMG1 is of great reference for the development of second-generation mRNA vaccines.
addition, the authors also indicate that the role and monitoring of SMG1 in the seven new corona subsypes vary from subsype to subsype, and even has no effect on one type of subsype. This suggests that the development of mRNA vaccines requires in-depth study of these relationships, and that non-mRNA vaccines need attention and cannot be replaced by each other. In addition, the KIAA1614 function has not yet been described in the literature, and an in-depth study of KIAA1614 can deepen the understanding of neo-crown pneumonia.
, on the other hand, the results are expected to be used directly to accurately detect new coronavirus. The authors say the accuracy of the test can be increased by sampling plasma and white blood cells and using simple formulas given in the article. Moreover, taking into account regional differences, different countries or regions can further refine and revise the formulas given in the text based on samples of local residents. Researchers could also use the new machine learning model proposed in the paper to try to find new key genes, or to further improve understanding of the new coronavirus by studying the association of other known genes associated with neo-corona pneumonia with the five key genes reported in the paper.
addition, to identify the new coronary pneumonia patients belong to what sub-type, help to cure the disease, to achieve personalized treatment. For example, controlling RIPK3 to a subsype is expected to achieve therapeutic results.
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