Gene regulation and control network constructing method based on Bayesian network

Abstract

The invention relates to a gene regulation and control network constructing method based on a Bayesian network. Relationship of all gene expressions in a species or a tissue is wholly analyzed and studied in simulation by constructing a gene regulation and control network model. The construction of the gene regulation and control network model comprises the following steps: A. identifying the best node order by a binary particle swarm optimization algorithm with memories, wherein after the particle speed is updated by the binary particle swarm optimization algorithm, the speed of part particles is varied and a searching space is searched, and skipping from local is taken as optimal; and B. inputting the obtained best node order as a K2 algorithm, then executing K2 algorithm and studying the structure of the Bayesian network. The method has the advantages of fast convergence, simple calculation, easy realization and the like, provides clues for studying regulation and control of gene transcription level, shows the structure of the gene regulation and control network more accurately and has important theoretical significance and practical value in many fields, such as biology, medicine science, pharmacy and the like.

Description

technical field [0001] The invention relates to the field of gene regulation network, more specifically, relates to a method for constructing gene regulation network based on Bayesian network. Background technique [0002] The study of gene regulatory network model analysis and reconstruction is a very important direction in bioinformatics research and an important means of gene expression data analysis. The purpose of gene network research is to conduct overall simulation analysis and research on the expression relationship of all genes in a certain species or tissue by establishing a gene transcription regulation network model, and to understand life phenomena under the framework of the system, especially the complex ones involved. The process of molecular regulation, the basic rules governing gene expression and function, the rules of information flow and the study of gene function in an overall framework provide predictive tools for the development and identification of ...

AI Technical Summary

Problems solved by technology

[0020] The main disadvantage of the prior art solution: the structure learning of the Bayesian network model refers to using the training sample set to determine the network topology, which is an NP-complete problem

If the K2 algorithm pre-selects a node in an inappropriate order, the correct rate of the constructed network structure is not high

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