Epistasis site mining method of an integer linear programming optimization Bayesian network

An integer linear programming and Bayesian network technology, applied in the field of bioinformatics, can solve problems such as inability to accurately and efficiently detect SNP sites, difficulty in calculating the range of genome-wide data, and low efficiency

Pending Publication Date: 2020-10-27
HUAZHONG AGRI UNIV
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However, these methods have a high false positive rate, and there are problems such as computational difficulties, high algorithm time complexity, and low efficie...

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  • Epistasis site mining method of an integer linear programming optimization Bayesian network
  • Epistasis site mining method of an integer linear programming optimization Bayesian network
  • Epistasis site mining method of an integer linear programming optimization Bayesian network

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[0035] 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.

[0036] see figure 1 and figure 2 , the embodiment of the present invention provides an integer linear programming optimization Bayesian network epistasis site mining method, including the following steps: S1, the SNP in the genotype data is represented by data in the form of 0 / 1 / 2, and the gene In the genotype data, the phenotype Class is represented by data in the form of 0 / 1, and SNP and Class are regarded as nodes forming a Bayesian network, and the SNP g...

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Abstract

The invention relates to an epistasis site mining method of an integer linear programming optimization Bayesian network. The epistasis site mining method comprises four steps S1-S4, which are: firstly, using conditional mutual information calculation, obtaining a Markov blanket of each node through three stages of expansion, contraction and consistency check, using the Markov blanket of each nodeas a candidate father node set of the node, and constructing all sub-structures of each node; then solving the score of each substructure by using a decomposable Bayesian network scoring function alpha-BIC; deleting some substructures by utilizing the property of a decomposable Bayesian network scoring function to obtain a candidate parent set after each node is screened and a Bayesian network score of the candidate parent set; finally, according to the candidate father sets and the scores, converting the constructed Bayesian network into an integer linear programming problem, and solving theglobally optimal Bayesian network with the highest score, including the SNP site and the phenotypic character, quickly by using two methods of branching and delimiting and cutting a plane, so that more effective and more accurate epistasis detection is realized.

Description

technical field [0001] The invention relates to the technical field of biological information, in particular to an epistasis site mining method for an integer linear programming optimization Bayesian network. Background technique [0002] With the improvement of modern medical level and the continuous development of molecular biology technology, diseases affecting human health mainly include Mendelian genetic diseases and complex diseases. Mendelian genetic diseases are single-gene diseases. Through the method of positional cloning and the laws of Mendelian inheritance, it is easy to determine the relevant genetic genes and clarify their inheritance methods. However, complex diseases are far more complex than Mendelian diseases. From the perspective of biogenetics, the genetic factors that determine complex biological traits mainly include three aspects: gene main effect, gene-gene interaction, and gene-environment relationship. interaction between. The complexity of non-M...

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Application Information

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IPC IPC(8): G16B20/20G16B40/00G16B5/20G06N7/00
CPCG16B20/20G16B40/00G16B5/20G06N7/01
Inventor 刘建晓杨轩杨晨雷继萌
Owner HUAZHONG AGRI UNIV
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