Method for mining epistasis loci of artificial bee colony optimized Bayesian network

Abstract

The invention relates to the technical field of bioinformatics and provides a method for mining epistasis loci of an artificial bee colony optimized Bayesian network, including four steps S1 to S4. The method for mining epistasis loci of the artificial bee colony optimized Bayesian network, comprises firstly using three stages of expansion, contraction, and symmetry detection to calculate the Markov blanket of nodes through conditional mutual information so as to construct an initial nectar source network structure; then, based on the initial nectar source, randomly adding, subtracting and reversing edges to generate new nectar source until the maximum number of initial nectar sources is reached. The three operations (collecting bees, observing bees, and reconnoitering bees) of artificialbee colony algorithm and the BIC and MIT scoring method of the Bayesian network are configured to evolve the structure of the Bayesian network, find the optimal network structure, quickly and accurately obtain the epistasis gene loci that affect phenotypic traits, and assist the gene function mining.

Description

technical field [0001] The invention relates to the technical field of biological information, in particular to a method for mining epistasis sites of an artificial bee colony optimization Bayesian network. Background technique [0002] With the improvement of people's quality of life, the improvement of the medical and health environment, and the rapid development of related biotechnology, the types of diseases with high incidence rates in humans have undergone tremendous changes. Malnutrition, infectious diseases and other diseases that are mainly affected by the environment have been effectively controlled, while complex diseases and Mendelian genetic diseases have become the main diseases that plague human beings. Mendelian genetic diseases are single-gene diseases, and their genetic process follows According to Mendel's law of inheritance, researchers have determined the relevant genetic genes by positional cloning, and basically clarified their inheritance methods. Co...

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Problems solved by technology

[0004]In recent years, researchers have proposed a variety of epistasis detection methods, but these methods still have problems such as computational difficulty, low efficiency, high algorithm complexity, and high false positive rate , leading to the inability to accurately and efficiently detect SNP sites and their combinations associated with diseases

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