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

An artificial bee colony optimization and Bayesian network technology, applied in the field of bioinformatics, can solve the problems of inaccurate and efficient detection of SNP sites, high false positive rate, difficult calculation, etc., and achieve the effect of assisting gene function mining

Active Publication Date: 2019-12-13
HUAZHONG AGRI UNIV
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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 efficien...

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

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[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0037] An embodiment of the present invention provides a method for mining epistasis sites of an artificial bee colony optimized Bayesian network, specifically:

[0038] 1. Use data in the form of 0, 1, and 2 to represent genotype data. For example, the data of SNP genotype AT is represented as follows: AA is represented by 0, TT is represented by 2, and AT / TA is represented by 1. 0 represents the common genotype of homozygosity, 1 represents the heterozygous ...

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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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Application Information

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IPC IPC(8): G16B40/00G16B20/00G06N3/00G06K9/62
CPCG16B40/00G16B20/00G06N3/006G06F18/295
Inventor 刘建晓杨晨高辉杨轩黄穗宇阚裕隆
Owner HUAZHONG AGRI UNIV
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