Missing mark filling method based on sliding window sparse convolution denoising auto-encoder

Abstract

The invention provides a missing marker filling method based on a sliding window sparse convolution denoising auto-encoder, which comprises the following steps: firstly, carrying out numerical conversion and one-hot coding on known gene data, dividing a training set and a verification set, then building a sparse convolution neural network model, carrying out missing marker filling on a gene sequence by adopting a segmented sliding window mode, and finally, obtaining a missing marker filling result; through overlapping of windows, central area prediction results with sufficient data features are obtained and spliced, and filling results of edge areas are abandoned. And then the filling precision of the missing mark is calculated, and the hyper-parameter of the neural network is adjusted according to the feedback result of the early-stage training. In practical application, gene filling results of multiple species such as corn and rice show that the filling precision of the method is remarkably higher than that of traditional algorithms such as KNN and SVD. The method is high in filling precision, has the advantages of simple model structure and high training efficiency, and has a wide application prospect in the field of gene sequence analysis.

Description

technical field [0001] The invention belongs to the technical field of computer and bioinformatics, and in particular relates to a missing marker filling method based on a sliding window sparse convolution denoising autoencoder. Background technique [0002] Genotype filling is a key step in the analysis of human, animal and plant genome sequences and can be used for genome-wide association analysis and genome-wide prediction. A large number of deletions are prevalent in genetic sequencing data due to a variety of reasons, including low call rates, deviations from Hardy-Weinberg equilibrium, and the presence of rare or low-frequency variants in the samples. Genotype imputation works by computationally inferring missing values ​​in a genotype distribution, typically using correlation or linkage information for untyped variants and markers near the genotype. The genetic variation in which the genotype is filled is mainly single nucleotide polymorphism (SNP), and other types o...

AI Technical Summary

Problems solved by technology

If only a general denoising autoencoder is used to encode and model a large number of markers on chromosomes, problems such as curse of dimensionality and overfitting will occur, making it impossible to effectively utilize the close linkage between markers in close proximity

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