Non-coded RNA and disease association prediction method based on sparse subspace learning

Abstract

The invention discloses a non-coded RNA and disease association prediction method based on sparse subspace learning and belongs to the field of system biology. The method comprises steps of 1, constructing an adjacency matrix associated with non-coded RNA-diseases, and then respectively calculating Gaussian spectrum kernel similarity of the non-coded RNA and Gaussian spectrum kernel similarity ofthe diseases; 2, calculating a graph theory characteristic matrix and a statistic characteristic matrix according to two similarity matrixes and an adjacent matrix, further constructing a target function and solving a mapping matrix G; and 3, solving non-coded RNA-disease association pair relationship score prediction matrixes, and performing sorting to give the final prediction result. The methodis advantaged in that a graph theory, a statistical method and a machine learning method are fused, the information of a negative sample in the non-coded RNA-disease associated data can be effectively utilized, the non-coded RNA with significant correlation to disease occurrence and development can be efficiently, accurately and quickly predicted, and problems of long time consumption and high cost of a biological experiment method are effectively solved.

Description

technical field [0001] The present invention relates to the field of systems biology, and more specifically, the present invention relates to a method for predicting association between non-coding RNA and disease based on sparse subspace learning. Background technique [0002] Non-coding RNA (non-coding RNA, ncRNA) refers to RNA molecules that do not encode proteins in the transcriptome, and common ones include microRNA, lncRNA, and circRNA. [0003] MicroRNAs (miRNAs) are endogenous single-stranded RNAs approximately 22 nucleotides in length that are found in a variety of species, including plants, animals and some viruses. As an important post-transcriptional regulator, they inhibit gene expression and promote mRNA degradation by base-pairing with the 3'untranslated regions (UTR) of target RNAs. They play key roles in diverse biological processes such as cell division, differentiation, development, metabolism, infection, aging, apoptosis and signal transduction. Experime...

AI Technical Summary

Problems solved by technology

However, since revealing associations by experimental methods is expensive and time-consuming, novel and efficient computational methods for association prediction are required

However, there are many common deficiencies in existing methods, such as failing to consider global similarity, high false positives involving transition components, or using randomized unvalidated samples as negatives, resulting in inaccurate approximate substitutions, etc.

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