MiRNA-mRNA target prediction method based on sequence statistical characterization learning

Abstract

The invention relates to a miRNA-mRNA target prediction method based on sequence statistical characterization learning, and belongs to the field of bioinformatics. Multi-scale and multi-granularity feature extraction is carried out on structural features of miRNA and mRNA sequences by adopting a mode of combining a neural network and an attention mechanism, so that the obtained features not only contain local and global multi-scale features of each base character of the sequences and a target region sequence, but also contain fine-granularity and coarse-granularity multi-granularity semantic information feature relation. And a variational automatic encoder structure is used as an overall framework, the prediction accuracy is ensured by utilizing probability distribution of input data, and the interpretability of the model is improved. According to the miRNA-mRNA target prediction method based on sequence statistical characterization learning, target prediction of miRNA-mRNA can be effectively completed.

Description

technical field [0001] The invention belongs to the field of bioinformatics, and relates to a miRNA-mRNA target prediction method based on sequence statistical representation learning. Background technique [0002] The expression process of Erna has always been an important research direction in the field of bioinformatics, and the prediction of miRNA targets that play an important regulatory role in the gene expression process of mRNA is more challenging, because the splicing or transcription that occurs in the process of gene regulation is a Dynamic. In the past decade, RNA therapy has been rapidly developed with the combined promotion of bioinformatics and deep learning. It can use functional nucleic acids to regulate the expression of pathogenic genes from the root to achieve the purpose of treating diseases. RNA therapy can be divided into three categories according to the mechanism of action (1) mRNA therapy encoding therapeutic protein or antigen; (2) small nucleic a...

AI Technical Summary

Problems solved by technology

Usually, when miRNA interacts with disease-causing genes, the accuracy and efficiency of miRNA recognition target sites and interactions on target sites are not ideal

Although the accuracy of predicted targets can be improved through sophisticated instrumentation and manual inspection, such methods are often costly and time-consuming

Even if the accuracy of target prediction is enhanced, the dynamic combination of feature expression during miRNA targeting will greatly affect the accuracy of the experiment

If the number of candidate targeting points is reduced, it will lead to incomplete prediction results and low generalization ability of the model

Adding neural network structure can make the model improve the screening ability of dynamic combination features, but the algorithm of this type of model focuses on the classification of target features, and does not consider the interconnection and joint action mechanism between different features

and lacks an interpretable learning path to guide the process of miRNA target prediction

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