Construction method of models of association relationships of SNP pathogenic factors and disease

A causative factor and correlation technology, applied in the field of data processing, can solve the problems of high modeling difficulty, accuracy impact, low model accuracy, etc., to achieve the effect of accurate correlation model, reduce the degree of mutual influence, and simple operation

Active Publication Date: 2019-11-15
XIDIAN UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

[0016] 1. The accuracy of the model established by the existing method is low;
[0017] 2. SNP data has

Method used

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  • Construction method of models of association relationships of SNP pathogenic factors and disease
  • Construction method of models of association relationships of SNP pathogenic factors and disease
  • Construction method of models of association relationships of SNP pathogenic factors and disease

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Experimental program
Comparison scheme
Effect test

Embodiment

[0143] A.Dat100 data set

[0144] The experimental data in this group come from the New York City Cancer Control Project. For this set of data, biologists embedded a total of 7 SNP pathogenic factors in the data Dat100 of 100 SNPs and 2000 samples, which are SNP pathogenic factors (98), (78), (6093), (4475), (8583100), (972047), (2581879299) (these 7 pathogenic factors are respectively numbered 1, 2,..., 7 below), the probability correlation relationship between each pathogenic factor and the disease are given.

[0145] The establishment of the relationship model between each SNP pathogenic factor and the disease of this group of data is completed by using the technology of the present invention. Figure 5 to Figure 8 Histogram of modeling results by AD-DTEM method for the first six causative factors in the Dat100 dataset.

[0146] The present invention also utilizes the real SNP pathogenic factors and disease correlation model known on this group of experimental data, and ...

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Abstract

The invention belongs to the technical field of data processing, and discloses a construction method of models of association relationships of SNP pathogenic factors and a disease. A sample data set corresponding to the current SNP pathogenic factor is collected; according to an initial value, absolute dividing is conducted on the sample data set; the association relationship of the current SNP pathogenic factor and the disease is modeled based on a machine learning method; the modeling result accuracy is evaluated; the model of the association relationship of the current SNP pathogenic factorand the disease is determined. According to the method, through the absolute dividing method, the degree of mutual influence among all the SNP pathogenic factors is reduced, so that the constructed model of the association relationship of each SNP pathogenic factor and the disease is more accurate. The method is easy to implement, the accurate models of the association relationships of the SNP pathogenic factors and the disease can be obtained simply by inputting original SNP data and all the SNP pathogenic factors.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a method for establishing an association relationship model between SNP pathogenic factors and diseases. Background technique [0002] Currently, the closest prior art: [0003] SNP: Single Nucleotide Polymorphisms (Single Nucleotide Polymorphisms), refers to the polymorphism caused by a single nucleotide (A, T, C, G) variation on the genome. More and more research evidence shows that SNP has a close relationship with diseases, and this relationship is the basis for understanding the causes of diseases, medical prevention and diagnosis. An in-depth understanding of the association between SNPs and diseases can provide the possibility to understand the pathogenic mechanism of diseases, and can also make a step forward in the treatment and victory of complex diseases. [0004] The research on the relationship between SNP and disease can be divided into two cat...

Claims

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Application Information

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IPC IPC(8): G16B20/20G16B40/20
CPCG16B20/20G16B40/20
Inventor 张军英朱皓晨
Owner XIDIAN UNIV
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