Non-coding RNA and disease relation prediction method based on Hessian regular non-negative matrix factorization

A technology of non-negative matrix decomposition and prediction method, applied in the field of systems biology, can solve problems such as failure to consider global similarity, high false positives for transition components, and inaccurate approximate substitution, so as to improve prediction performance and reduce human and material resources. The consumption of , the effect of reducing difficulty

Active Publication Date: 2019-12-10
ARMY MEDICAL UNIV
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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
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  • Non-coding RNA and disease relation prediction method based on Hessian regular non-negative matrix factorization
  • Non-coding RNA and disease relation prediction method based on Hessian regular non-negative matrix factorization
  • Non-coding RNA and disease relation prediction method based on Hessian regular non-negative matrix factorization

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[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiment is only an embodiment of the present invention about miRNA, not all Example (ncRNA also includes other types, such as lncRNA, circRNA, etc.). Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0051] The known human miRNA-disease association data used in the embodiments of the present invention are from the database HMDDV2.0 (URL http: / / www.cuilab.cn / hmdd ) and then downloaded, after cleaning, classifying and normalizing the downloaded data, 5430 experimentally validated human miRNA-disease associations can be obtained, including 383 diseases and 495 miRNAs.

[0052] Then...

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Abstract

The invention discloses a non-coding RNA and disease relationship prediction method based on Hessian regular non-negative matrix factorization, and belongs to the field of system biology. The method mainly comprises the following three steps: 1, respectively calculating Gaussian spectrum kernel similarity of non-coding RNA and Gaussian spectrum kernel similarity of diseases; 2, calculating a prediction score of the non-coding RNA-disease association pair by using an iterative solution algorithm; 3, sorting the scores according to the calculated non-coding RNA-disease association, and giving afinal prediction result. According to the method, the internal manifold structure of the data is described meticulously through Hessian regularization, so that the information of a negative sample iseffectively utilized; the l2, 1 norm constraint and the approximate orthogonal constraint ensure the group sparsity of the coding matrix, and the influence of noise data can be weakened. According tothe method, a relatively reliable prediction result can be obtained, and the problems of long consumed time and high cost of a biological experiment method are effectively solved.

Description

technical field [0001] The invention relates to the field of systems biology, and more specifically, the invention relates to a method for predicting the relationship between non-coding RNAs and diseases based on Hessian canonical non-negative matrix decomposition. 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 trans...

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Application Information

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IPC IPC(8): G16H50/50G16B20/00
CPCG16H50/50G16B20/00
Inventor 汤永易东伍亚舟李高明
Owner ARMY MEDICAL UNIV
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