Feature selection method facing to SNP (Single Nucleotide Polymorphism) data
A feature selection method and data technology, applied in electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as low algorithm efficiency
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Embodiment 1
[0061] see figure 1 , this feature selection method for SNP data is characterized in that the specific steps are as follows:
[0062] (A) Perform data preprocessing;
[0063] (B), using the redesigned Relief algorithm to eliminate irrelevant SNP features;
[0064] (C), using the improved SVM-RFE algorithm to sort the criticality of the SNP features;
[0065] (D) Screening of key SNPs using cross-validation.
Embodiment 2
[0067] refer to Figure 1 to Figure 5 , the present invention is a kind of feature selection method facing SNP data, here take the SNP data feature of hypertensive patients as an example, its specific steps are as follows:
[0068] (1) Perform data preprocessing, such as figure 2 As shown, the specific steps are as follows:
[0069] a) Categorical labeling: SNP data belongs to two-category samples, that is, there are only two types of samples: the disease-case group and the healthy-normal group, the case group is labeled with a category label {+1}, and the normal group is labeled with a category label {-1};
[0070] b) Data coding: The SNP typing results detected by the gene chip have four forms: wild homozygous AA, mutant heterozygous AB, mutant homozygous BB, and typing failure marks NC; AA is coded as 0, AB is coded as 1, BB is coded as 2; NC is cleaned during the data cleaning process and is not coded;
[0071] c) Data cleaning: NC is noise data in SNP data analysis. W...
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