Zhang, Yan; Chen, Zhaohui On the genetic regulation of Bayesian networks. (Chinese. English summary) Zbl 1463.92046 Math. Pract. Theory 50, No. 8, 84-93 (2020). Summary: To study the network regulatory relationship of genes, the knowledge of graph theory is combined by the decision tree algorithm and Bayesian network method. The Bayesian network model of genes is constructed effectively and the reasoning is carried out. For a group of leukemia data, firstly, the data preprocessing such as standardization and discretization is carried out. Secondly, the order of the nodes between genes is obtained by the decision tree ID3 algorithm. And the structure learning of the Bayesian network is studied by the K2 algorithm to find out the network topology of genes. Then the parameter learning is used by the maximum likelihood estimation to find out the probability dependence relationship between the parent nodes and the child nodes in the network. Finally, the validity of the Bayesian network model is verified. The analysis of the test data shows that the Bayesian network has high accuracy in the prediction and analysis of the regulatory relationship of genes. MSC: 92D10 Genetics and epigenetics 92C40 Biochemistry, molecular biology 92C42 Systems biology, networks 05C90 Applications of graph theory Keywords:genetic regulation; Bayesian network; structure learning; parameter learning PDFBibTeX XMLCite \textit{Y. Zhang} and \textit{Z. Chen}, Math. Pract. Theory 50, No. 8, 84--93 (2020; Zbl 1463.92046)