Prediction method for correlation between circular RNA and disease based on gradient enhancement decision-making tree

Abstract

The invention discloses a prediction method for a correlation between circular RNA and a disease based on a gradient enhancement decision-making tree. A circular RNA-disease relationship network is converted into an undirected graph, the circular RNA base sequence similarity, the function annotation semantic similarity and the expression similarity are calculated, and the disease function similarity and the semantic similarity are calculated; a multi-network integration algorithm is adopted for integrating a plurality of kinds of circular RNA similarity networks and conducting weighted averageintegration on a disease similarity network, statistical characteristics of the integrated circular RNA and disease similarity network and the circular RNA-disease relationship network are extracted,and the integrated circular RNA and disease similarity network is converted into an unweighted graph-related characteristic, a circular RNA base sequence characteristic and a circular RNA-disease relationship network implicit vector characteristic; a gradient enhancement decision-making tree learning machine is trained, and a potential circular RNA-disease relationship is predicted. By means of the method, the potential circular RNA-disease relationship can be accurately predicted; and the prediction accuracy of the circular 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 diseases based on a gradient enhanced decision tree. Background technique [0002] Circular RNA is a short non-coding RNA. There is no free 5'-cap end and 3'-polaydenylated tail structure in the circular RNA molecule, but a closed circular structure. The linear RNA at the 'cap end and the 3' tail end, this is the biggest difference. It is precisely because of this closed circular structure that it is more difficult for circular RNAs to be detected in organisms. At the same time, the closed circular structure makes circular RNA more stable than linear RNA, which can be used as a biomarker for certain diseases. With the development of base sequence detection technology and high-throughput technology, more and more relationships between circular RNAs and diseases have been revealed. Many r...

AI Technical Summary

Problems solved by technology

Although the traditional RNA base sequence calculation technology has been widely used in the detection of disease-related genes and verified by high-throughput technolo

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