Multidata integration circular RNA and disease correlation prediction method based on double random walk restart

Abstract

The invention discloses multidata integration circular RNA and a disease correlation prediction method based on double random walk restart. By transforming a circular RNA-disease relationship networkinto an undirected graph, calculating the semantic similarity of circular RNA function annotations, structural similarity and functional similarity, and calculating the disease function and semantic similarity, multiple circular RNA similarity networks and disease similarity networks are integrated into a comprehensive circular RNA similarity network and disease similarity network, a random walk restart algorithm is applied to the integrated circular RNA similarity network and the disease similarity network separately, the cold restart problem is avoided, and a potential circular RNA-disease relationship is predicted. Accordingly, the potential circular RNA-disease relationship can be accurately predicted; simulation experiment results indicate that the precision, recall rate, accuracy, f1-measure and other indexes are better; compared with other relationship prediction methods, the prediction accuracy of the RNA-disease relationship is improved.

Description

technical field [0001] The invention belongs to the technical field of biological information, and in particular relates to a method for predicting the correlation between circular RNA and disease based on double random walk restart. Background technique [0002] Recently, a new biomolecule, circular RNA, has attracted much attention. Circular RNA is a relatively novel biomolecule that participates in various activities of biological life and controls gene expression. Unlike linear RNA with free 3' and 5' ends, the structure of circular RNA is a closed loop structure, neither free 5'-cap end nor 3'-polaydenylated tail structure. The first circular RNAs were discovered in plant viruses. Due to the stable loop structure and low expression levels, circRNAs are often identified as molecular fragments or byproducts of transcription. However, with the development of high-throughput sequencing technology, more and more circular RNAs have been gradually discovered. At the same t...

AI Technical Summary

Problems solved by technology

[0005] Although high-throughput sequencing technology has been applied to the identification of the relationship between circular RNA and disease, there are some limitations that cannot be ignored

Although these techniques can extract circRNA-disease relationships with high accuracy, they are still time-consuming and costly

What's more, fewer computational methods for predicting potential circRNA-disease potential relationships are another major motivation

Images