Multi-data integration circular RNA and disease correlation prediction method based on double random walk restart

Abstract

The invention discloses a multi-data integration circular RNA-disease correlation prediction method based on double random walk restart, by converting the circular RNA-disease relationship network into an undirected graph, calculating the circular RNA functional annotation semantic similarity, Structural similarity and functional similarity, calculate disease function and semantic similarity, integrate multiple circular RNA similarity networks and disease similarity networks into a comprehensive circular RNA similarity network and disease similarity network, and random walk The restart algorithm is applied to the integrated circular RNA similarity network and disease similarity network respectively to avoid the cold start problem and predict potential circular RNA-disease relationships. The method of the present invention can accurately predict the potential circular RNA-disease relationship; the simulation experiment results show that the precision, recall rate, accuracy, f1-measure and other indicators are better; compared with other relationship prediction methods, the circular RNA Prediction accuracy of RNA‑disease relationships.

Description

technical field [0001] The invention belongs to the technical field of biological information, and in particular relates to a multi-data integration circular RNA and disease correlation prediction method 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 the expression of genes. Unlike linear RNAs, which have free 3' and 5' ends, circular RNAs have a closed-loop structure with neither a free 5'-cap end nor a 3'-polaydenylated tail structure. The first circular RNAs were found in plant viruses. Due to stable loop structures and low expression levels, circular RNAs are often identified as molecular fragments or by-products of transcription. However, with the development of high-throughput sequencing technology, more and more circular RNAs have been gradually discovered. ...

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

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