Insulator element prediction system based on semi-supervised deep learning

Abstract

The invention discloses an insulator element prediction system based on semi-supervised deep learning. The system comprises an extraction module, a coding module, a training module and an analysis module. The extraction module, the coding module, the training module and the analysis module are connected in sequence; the extraction module is used for extracting a chromosome number sequence in DNA;the coding module is used for intercepting a sequence and coding the intercepted sequence; the training module is used for training and generating an insulator element prediction model; and the analysis module is used for identifying and analyzing an insulator sequence in the DNA chromosome sequence through the trained insulator element prediction model. According to the method, the insulator element prediction model is established through the combination of the semi-supervised stepped network and the convolutional neural network, so the insulator sequence in the DNA sequence can be effectively and accurately identified; meanwhile, the insulator element identification cost and procedures are effectively reduced.

Description

technical field [0001] The invention relates to the field of biological insulator prediction, in particular to an insulator component prediction system based on semi-supervised deep learning. Background technique [0002] Chromatin insulators are DNA-protein complexes that have a wide range of functions in nuclear biology. In general, insulators are located between enhancers or promoters and genes, and are used to reduce or block gene expression, or as heterochromatin Insulator elements are of great significance in gene therapy. At present, there is a big obstacle in the field of gene therapy, which is genotoxicity and gene mutation caused by insertion operations. Effectively finding short-segment insulator elements can hinder Or regulate the expression of disease-causing genes to improve the safety of gene therapy. [0003] The traditional way to verify insulator fragments is through cell experiments, which is not only inefficient but also expensive. Known bioinformatics ...

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Problems solved by technology

[0004] The purpose of the present invention is to: aim at the above existing problems, to provide a semi-supervised deep learning based insulator component prediction system and method; the present invention solves the problem of low verification efficiency and high cost of insulator fragments; solves the inability to effectively extract insulators Problems with features inside components

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