Genetic locus excavation method based on multi-target ant colony optimization algorithm

An ant colony optimization algorithm and gene locus technology, applied in the field of information processing, to achieve the effect of improving the positioning speed, improving the robustness, and reducing the false positive rate and false negative rate

Inactive Publication Date: 2015-12-30
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

In the present invention, the interaction between sites is added to the multi-objective model; aiming at the specific characteristics of GWAS data, the encapsulation feature selection method based on ant colony optimi

Method used

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  • Genetic locus excavation method based on multi-target ant colony optimization algorithm
  • Genetic locus excavation method based on multi-target ant colony optimization algorithm
  • Genetic locus excavation method based on multi-target ant colony optimization algorithm

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Embodiment 1

[0025] Such as figure 1 As shown, this embodiment first uses the ant colony algorithm as the basis of the packaged feature selection algorithm to find feature subsets; secondly, uses two methods of logistic regression and Bayesian network as the two objectives of the multi-objective optimization algorithm Carry out modeling evaluation on the previously found feature subsets and corresponding class labels; then use multi-objective optimization theory to optimize to obtain non-dominated solution sets, and perform ant colony algorithm iteration according to the characteristics of non-dominated solution sets screened by multi-objective optimization; Finally, the chi-square test was used to perform an exhaustive hypothesis test on the previously screened characteristics to obtain the genetic loci associated with complex traits according to the preset P value.

[0026] The implementation of this embodiment is based on the following virtual genome-wide association datasets:

[0027]...

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Abstract

The invention provides a genetic locus excavation method based on a multi-target ant colony optimization algorithm. The ant colony optimization is used as a foundation design packaging type feature selection algorithm; in each type of iteration, one manual ant selects one SNPs feature subset, one feature subset and corresponding complicated property states to construct a multi-target model; a logistic regression model and a Bayesian network model are used for modeling the selected SNPs feature subset and the corresponding property states respectively; then an AIC (Akaike Information Criterion) and K2 are used as evaluation criteria of the corresponding models, and scores are used as solutions of a multi-target function; a non-dominated ranking method is adopted and all the solutions of the multi-target model in the step 2 are screened into non-dominated solutions and dominated solutions; the iteration of an pheromone matrix Tau is carried out according to advantageous and disadvantageous degrees of the solutions, and one SNPs locus subset selected by the feature selection algorithm is obtained after the iteration is finished. An endless hypothesis test is carried out on Chi-square analysis in the SNPs locus subset; finally, SNPs locus related to complicated properties is screened according to a P value set by a user.

Description

technical field [0001] The present invention relates to a technology in the field of information processing, specifically a gene locus mining algorithm, which is to find and locate SNPs loci related to complex traits in the whole genome association data, especially a method based on ant colony optimization algorithm and Gene locus mining algorithm of multi-objective optimization algorithm. Background technique [0002] The location of complex trait-related gene loci is an important basis for the genetic improvement of animals and plants and the pathogenesis of complex human diseases. It is one of the hot issues in the field of bioinformatics, and genome-wide association analysis methods are usually used. The basic principle of genome-wide association analysis is: select a certain number of case groups (case) and control group (control) in a certain population, and then scan all SNP sites in the whole genome of all samples, and compare their alleles or genes. The difference ...

Claims

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Application Information

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IPC IPC(8): G06F19/12G06F19/24
Inventor 沈红斌景鹏杰
Owner SHANGHAI JIAO TONG UNIV
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