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MiRNA-disease association predicting method based on double random walk models

A prediction method, a double-random technology, applied in the interdisciplinary field of bioinformatics and artificial intelligence, can solve problems such as time-consuming, prediction accuracy needs to be improved, waste of manpower and financial resources, etc., and achieve the effect of rich information

Pending Publication Date: 2019-06-25
GUILIN UNIV OF ELECTRONIC TECH
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Problems solved by technology

For example, traditional biological experimental methods to determine miRNA-disease associations require a lot of time, wasting manpower and financial resources
Based on machine learning and network forecasting methods, the forecasting accuracy needs to be improved

Method used

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  • MiRNA-disease association predicting method based on double random walk models
  • MiRNA-disease association predicting method based on double random walk models
  • MiRNA-disease association predicting method based on double random walk models

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Embodiment

[0055] refer to figure 1 , a miRNA-disease association prediction method based on a double random walk model, comprising the following steps:

[0056] 1) Obtain the miRNA-disease association data set and construct an adjacency matrix about the miRNA-disease association: obtain the miRNA-disease association data confirmed by biological experiments from the HMDD database, and obtain 5430 pairs of different miRNA and disease association data, which involve disease There are 383 kinds of miRNAs and 495 kinds of miRNAs. Define D={d(1), d(2), d(3),...,d(nd)} to record the set of nd diseases, M={m( 1), m(2), m(3),..., m(nm)} to record the set of nm miRNAs, and build an adjacency matrix MD na×nm Represents the relationship between miRNA and disease association data, when disease d(i) and miRNAm(j) are verified as association, the adjacency matrix MD nd×nm The value of MD(i, j) is set to 1; otherwise, the value of MD(i, j) is set to 0, indicating an unknown association;

[0057] 2)...

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Abstract

The invention discloses a miRNA-disease association predicting method based on double random walk models. The method is characterized by comprising the following steps of 1), acquiring a known miRNA-disease association dataset, and establishing an adjacent matrix which is associated with miRNA-disease; 2), respectively constructing Gaussian interaction attribute kernel similarity matrix of the miRNA and the disease; 3), constructing a miRNA function similarity matrix and a disease meaning similarity matrix; 4), integrating similarities of the disease and the miRNA by means of a similar networkfusion algorithm; and 5), predicting the miRNA-disease association relation by means of the double random walk models. The miRNA-disease association predicting method has advantages of low cost and short time consumption. Furthermore the predicting precision of the miRNA-disease association predicting method is higher than that of existing methods.

Description

technical field [0001] The invention relates to the interdisciplinary field of bioinformatics and artificial intelligence, in particular to a miRNA-disease association prediction method based on a double random walk model. Background technique [0002] MicroRNAs (miRNAs) are a class of small endogenous non-coding RNAs with a length of about 20-24 nucleotides, which combine with the 3′ non-coding region of the target mRNA through base pairing, resulting in the target mRNA degradation or translation mechanisms, thereby regulating gene expression at the transcriptional level. More and more studies have shown that miRNA plays a very important role in biological processes such as transcription, immune response, cell proliferation, and cell differentiation. MiRNA dysfunction and miRNA mutation may lead to various diseases. Therefore, it is very important to identify the interaction between miRNA and disease, which will help humans understand disease mechanism, disease prevention ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/50G16B5/00
Inventor 樊永显朱庆祺张向文张龙
Owner GUILIN UNIV OF ELECTRONIC TECH
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