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Intergenic interaction relation excavation method based on Bayesian network reasoning

A Bayesian network and relationship mining technology, applied in the field of biological information, can solve problems such as gene-environment interaction incompatibility, and achieve the effect of assisting gene function mining

Active Publication Date: 2019-03-01
HUAZHONG AGRI UNIV
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Problems solved by technology

However, because the gene-gene and gene-environment interactions do not conform to Mendelian inheritance laws, it also brings great difficulties and challenges to researchers.

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  • Intergenic interaction relation excavation method based on Bayesian network reasoning
  • Intergenic interaction relation excavation method based on Bayesian network reasoning
  • Intergenic interaction relation excavation method based on Bayesian network reasoning

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[0037] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0038] 1. Normalize gene expression transcription data and specific phenotype data, and process them as continuous data in a specific interval (such as [-3,3]).

[0039] 2. Use the three-stage dependency analysis Bayesian network structure learning method to construct a Bayesian network structure including gene and phenotypic trait nodes, mainly including three steps of Drafting, Thickening and Thinning.

[0040] (1) The initial network graph is empty Consider genes and specific phenotypic traits as different nodes in the network, let gene set G, phenotypic trait set P, for any gene ge i ∈G, any...

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Abstract

The present invention provides an intergenic interaction relation excavation method based on Bayesian network reasoning. The method comprises the following steps of: 1, employing a method of estimation of entropy by employing a Gaussian kernel probability density estimation quantity to calculate interaction information between genes, between genes and phenotypic characters and between phenotypes and the phenotypic characters; 2, employing a three-stage dependence analysis Bayesian network structure learning method to construct a Bayesian network including genes and phenotypic character nodes;3, employing the Bayesian estimation parameter learning method to perform parameter learning to obtain a contingent probability form between nodes; and 4, employing a Gibbs sampling Bayesian network approximate reasoning method to calculate the contingent probabilities of genes with different quantities and the phenotypic characters, and obtaining an intergenic interaction relation influencing thespecial phenotypic characters according to the calculation result. The intergenic interaction relation excavation method based on Bayesian network reasoning can help biology researchers of obtainingof epistasis genetic locuses influencing the special phenotypic characters to assist in gene function excavation and provide reference for hereditary basis analysis of complex quantitative charactersof different species.

Description

technical field [0001] The invention relates to the technical field of biological information, in particular to a method for mining interaction relationships between genes based on Bayesian network reasoning. Background technique [0002] Bioinformatics is a comprehensive application of biology (such as structural biology, biochemistry, genetics, etc.), computer science (artificial intelligence, machine learning, etc.), mathematics (probability and statistics, etc.) The interdisciplinary science behind the mystery of biological data. It comprehensively uses various algorithms, software, etc. to analyze and process a large amount of biological data, and then dig out the biological laws hidden behind the data. Disease is the most important factor affecting human health. Disease can be divided into simple disease and complex disease. At present, most diseases are complex diseases, such as Alzheimer's disease, asthma, Parkinson's disease, osteoporosis, connective tissue diseas...

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Application Information

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IPC IPC(8): G16B40/00G16B20/00
Inventor 刘建晓向宇嘉田宗霖郝松林张小龙
Owner HUAZHONG AGRI UNIV
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