Self-avoiding random walk-based disease-associated miRNA prediction method and system

Abstract

The invention discloses a self-avoiding random walk-based disease-associated miRNA prediction method and system. According to the method of the invention, self-avoiding random walk is adopted to traverse a disease-miRNA bipartite graph, and the association degree of two nodes is measured through the ratio of two attributes (the transition probability and average step size of the two nodes) of theself-avoiding random walk, and therefore, the association between a disease and miRNAs is realized. The method can be used for both an unweighted miRNA-disease bipartite graph and a weighted miRNAs-disease bipartite graph. With the method adopted, disease-associated miRNAs can be accurately predicted based on known miRNA-disease association information, a large number of pathogenic miRNAs can be predicted at one time, and problems such as high cost and time-consuming performance of a biological experimental method can be solved.

Description

technical field [0001] The invention belongs to the field of systems biology, and in particular relates to a disease-associated miRNA prediction method and system based on self-avoidance random walk. Background technique [0002] MicroRNAs (miRNAs) are a class of non-coding RNAs with a length of about 19-24 nucleotides, which can regulate gene expression at the post-transcriptional level by complementary binding to the mRNA 3'-UTR, leading to target mRNA degradation or translational repression. In recent years, studies have shown that it is the dysfunction of miRNA that leads to the abnormal expression of the genes it regulates, which in turn leads to the occurrence and development of diseases, especially in solid tumors. Therefore, effectively identifying the relationship between miRNAs and diseases has very important theoretical and practical significance for studying the mechanism of diseases and providing new biological targets for the prevention and treatment of complex...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to propose a disease-associated miRNA prediction method based on self-avoiding random walk, which only needs to be based on known experimentally verified miRNA-disease association information can predict new miRNA-disease associations more accurately, and a large number of pathogenic miRNAs can be predicted at one time, which solves the problems of expensive and time-consuming biological experimental methods

Images