Method for predicting miRNA-disease association relationship based on graph neural network

A technology of association relationship and prediction method, applied in the field of bioinformatics, can solve the problems of low prediction accuracy and achieve the effect of improving prediction accuracy and avoiding deviation

Active Publication Date: 2021-09-17
XIDIAN UNIV
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Problems solved by technology

[0007] The purpose of the present invention is to overcome the deficiencies of the above-mentioned prior art, and propose a miRNA-disease correlati

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  • Method for predicting miRNA-disease association relationship based on graph neural network
  • Method for predicting miRNA-disease association relationship based on graph neural network
  • Method for predicting miRNA-disease association relationship based on graph neural network

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Embodiment Construction

[0043] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the present invention does not belong to the object of non-granting patent right stipulated in Article 25 of the Patent Law, and also conforms to the second paragraph of Article 2 of the Patent Law. Provisions:

[0044] refer to figure 1 , the present invention comprises the steps:

[0045] Step 1) Obtain miRNA-disease association relationship data L:

[0046] Download from the miRNA-disease association database HMDDv2.0 and M kinds of miRNA r={r 1 ,r 2 ,...,r m ,...,r M} Associated U kinds of diseases d={d 1 , d 2 ,...,d u ,...,d U}’s R pieces of miRNA-disease association data L={L 1 , L 2 ,...,L r ,... L R}, each miRNAr m associated with at least one disease, and each disease d u Associated with at least one miRNA, where, M≥100, r m Indicates the mth miRNA, N≥100, d u Indicates the uth disease,...

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Abstract

The invention provides a method for predicting a miRNA-disease association relationship based on a graph neural network. The method comprises the following implementation steps: acquiring miRNA-disease association data; constructing a miRNA-disease association network; acquiring a sample data set and a label data set; extracting an h-order closed subgraph of each miRNA-disease node pair; acquiring a node feature matrix of each h-order closed sub-graph; obtaining a training sample set, a training sample label data set and a predicted sample set; building a graph neural network; performing iterative training on the graph neural network; and obtaining a miRNA-disease associated prediction result. According to the method, the graph structure feature information is learned from the miRNA-disease association network by using the graph neural network, so that the problem of low prediction accuracy in the prior art is solved, the miRNA-disease association prediction accuracy is improved under the condition of not using similarity data, and the method can be used for predicting potential miRNA-disease association.

Description

technical field [0001] The invention belongs to the technical field of biological information, and relates to a method for predicting miRNA-disease correlation, in particular to a graph neural network-based prediction method for miRNA-disease correlation. Background technique [0002] miRNA is a class of non-coding single-stranded RNA molecules composed of 20-25 nucleotides. microRNA can participate in a wide range of important biological processes and play the role of feedback mechanism, such as cell division, differentiation, apoptosis, cell cycle regulation, inflammation and stress response. Disregulation of miRNA (including expression disorder, function increase or decrease mutation, epigenetic silencing, etc.) often leads to abnormal biological levels in the human body and leads to the occurrence of many diseases. Therefore, identifying disease-associated miRNAs can improve human understanding of complex diseases. [0003] Using the method of biological experiments to...

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Application Information

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IPC IPC(8): G16B40/20G06N3/04G06N3/08
CPCG16B40/20G06N3/084G06N3/045Y02A90/10
Inventor 鱼亮巨秉熠
Owner XIDIAN UNIV
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