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Identification of structural key genes of mutual information gene networks of brain tumor

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  • Wang, Qingyun
  • Xiao, Yayuan
  • Meng, Dazhi

Abstract

Identifying genes associated with specific diseases plays an important role in the pathological study, diagnosis, and treatment of diseases. In this paper, we propose a new method to identify key genes for any specific disease—the mutual information gene network (MIGN)-structural key gene (SKG). Considering brain tumors as an example, we identified four types of 37 genes that have varying ”behaviors” in MIGNs of normal cells and different grades of tumor cells, called SKGs, which are closely related to brain tumors. Using SKGs and K-means clustering algorithm for testing, the test accuracy rate was approximately 94.56%. MIGN-SKG effectively identifies a subset of genes that may be markers of disease progression or therapeutic targets for the disease. The key innovation of MIGN-SKG is that it is unrestricted by differentially expressed genes and it directly identifies key genes from the perspective of changes in genetic relationships during disease progression. It can identify potential key genes for any specific disease as well as other dynamic biological systems.

Suggested Citation

  • Wang, Qingyun & Xiao, Yayuan & Meng, Dazhi, 2022. "Identification of structural key genes of mutual information gene networks of brain tumor," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P2).
    • Handle: RePEc:eee:phsmap:v:608:y:2022:i:p2:s0378437122008809
    DOI: 10.1016/j.physa.2022.128322
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