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MiRNA-disease association prediction method, system, terminal and storage medium

A prediction method and disease technology, applied in the field of bioinformatics, can solve problems such as inability to use association prediction, low prediction accuracy, and inability to fully characterize the complex relationship of miRNAs, and achieve high cost, high accuracy, and time-consuming problems. Effect

Pending Publication Date: 2020-09-18
SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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  • Application Information

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Problems solved by technology

[0005] The embodiment of the present application provides a miRNA-disease association prediction method, system, terminal, and storage medium, aiming at at least to a certain extent solving the problem of miRNA-disease association prediction calculation methods in the prior art only considering the relationship between miRNA and disease One-sided information, inability to fully characterize the complex relationship between miRNAs and diseases, low prediction accuracy, and technical problems that cannot be used for association prediction between diseases and miRNAs without known associations

Method used

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  • MiRNA-disease association prediction method, system, terminal and storage medium
  • MiRNA-disease association prediction method, system, terminal and storage medium
  • MiRNA-disease association prediction method, system, terminal and storage medium

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Embodiment 1

[0087] The following are the implementation steps of the miRNA-disease association prediction method according to the second embodiment of the application. The second embodiment of the application selects the disclosed data as test data, and uses a 5-fold cross-validation method to evaluate the technical solution of the application. Specifically:

[0088] S1: Obtain multi-source information related to miRNA-disease from the database;

[0089] S2: Construct miRNA-disease association matrix MD based on miRNA-disease association data;

[0090] In this step, after removing duplicate data from the test data, there are a total of 577 miRNAs and 336 diseases, with 6,441 associations. When constructing the miRNA-disease association matrix, all associations are randomly divided into 5 parts, of which 4 parts are used as the training set and 1 part is used as the test set. The association in the test set is set to 0 in the miRNA-disease association matrix, and the cycle is repeated 5 times. ...

Embodiment 2

[0100] In order to further evaluate the true performance of this application for predicting miRNA-disease associations, the following examples have conducted case studies on pancreatic cancer. In the case, all known associations related to pancreatic cancer are marked as unknown, and the model's ability to find these associated miRNAs is then studied. The top 50 miRNAs with the highest prediction scores are selected for verification in the dbDEMC2 and PhenomiR2 databases, which store the experimentally confirmed associations between miRNAs and diseases. It can be seen from the verification results that the 50 miRNAs (shown in Table 1) related to pancreatic cancer predicted by this application are all found in the database, which further proves the practicality of the model and the model can be used without any known Related miRNA predictions for related diseases.

[0101]

[0102]

[0103] See image 3 , Is a schematic structural diagram of the miRNA-disease association predicti...

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Abstract

The embodiment of the application relates to a miRNA-disease association prediction method, a system, a terminal and a storage medium. The method comprises the following steps of: constructing a miRNA-disease association matrix, a miRNA similarity matrix and a disease similarity matrix according to miRNA-disease related data; constructing a heterogeneous network according to the miRNA-disease association matrix, the miRNA similarity matrix and the disease similarity matrix; learning topological information of the heterogeneous network by adopting a neural network, calculating optimal parameters of the heterogeneous network through topology preservation, and reconstructing the heterogeneous network according to the optimal parameters; the reconstructed heterogeneous network is the correlation score matrix of miRNA and diseases. The embodiment of the application solves the problems of high cost and time consumption of a biological experiment method, improves the effect of miRNA-disease association prediction, has practicability, and can be used for association prediction between diseases without known association and miRNA.

Description

Technical field [0001] The embodiments of the present application belong to the technical field of bioinformatics, and particularly relate to a miRNA-disease association prediction method, system, terminal, and storage medium. Background technique [0002] miRNAs are small endogenous non-coding RNAs with a length of about 22 nucleotides, which inhibit the expression of target genes by inducing messenger RNA degradation, translation inhibition or other morphological regulation mechanisms. A lot of research evidence shows that miRNAs play an important role in many biological processes. MiRNA dysfunction and miRNA mutations can lead to various diseases. Therefore, identifying the interaction between miRNAs and diseases is helpful for humans to understand the mechanism of the disease, thereby providing help for the prevention and treatment of the disease. [0003] Biological experimental methods require a lot of resources and time costs. Therefore, many computational methods for predi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16B20/00G16B40/00G16H50/70G06K9/62
CPCG16B20/00G16B40/00G16H50/70G06F18/22
Inventor 朱荣祥吴红艳蔡云鹏纪超杰
Owner SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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