Gene trait identification method based on torch supervised deep learning

Abstract

The invention relates to the technical field of biological information, in particular to a method for identifying gene characters on the basis of Torch supervised deep learning. The technical scheme includes that the method comprises steps of firstly, preprocessing gene PNG (portable network graphic) image information data to enable the gene PNG image information data to conform to Tensor data objects for Torch deep learning model training; secondly, building deep learning models by the aid of Torch, training Tensor training data in deep convolutional neural network models to obtain perfect training data identification results and then outputting tag vectors and weight parameters; thirdly, extracting weight parameters of various connections of a certain category of result tags after all training is completed and inversely coding gene image data information to obtain related SNP (single nucleotide polymorphism) loci of characters corresponding to genes and corresponding weight parameters. The method has the advantages that the characters corresponding to the genes can be conveniently and intelligently classified by the aid of the method, genes with unknown characters can be subjected to self-identification, and the like.

Description

【Technical field】 [0001] The invention relates to the technical field of biological information, in particular to a gene trait identification method based on Torch supervised deep learning. 【Background technique】 [0002] The Human Genome Project laid the foundation for studying diseases from genes, and people hope to find the relationship between human diseases and genes. The basic principle of Genome-Wide Association Study (GWAS, Genome-Wide Association Study) is to select a certain statistical number of samples from the case group and the control group in the same population, and compare the SNP loci in the case group and the control group within the whole genome. If the frequency of a certain SNP site in the case group is significantly higher or lower than that in the control group, it is considered that the SNP site is associated with complex diseases. Although GWAS has discovered many SNPs associated with complex diseases, there are still many problems in GWAS, and th...

AI Technical Summary

Problems solved by technology

However, BLAST also has certain limitations. It cannot perform gene classification and self-identification according to corresponding traits under deep learning, and it is powerless to intelligently classify corresponding traits and self-identify for large-scale genetic data sets.

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