Drug-disease association prediction method and system

A prediction method and disease technology, applied in the direction of drug reference, etc., can solve the problems of complex implementation, lack of sufficient mining methods, and large sparsity of association matrix data.

Active Publication Date: 2021-07-20
GUANGDONG UNIV OF TECH
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Problems solved by technology

However, all the methods applied to drug repositioning so far directly use the information of the unprocessed original drug-disease association matrix. Since only a small part of these similar association information has been proved and recorded in actual situations, there is still There is a lot of similar correlation information that needs to be proved urgently, resulting in the sparseness of the original correlation matrix data used in the prediction method
In addition, for the extraction of similarity features between drugs and diseases, there is still a lack of sufficient mining methods
[0003] The publication date is 2021.03.26, and the announcement number is CN112562795A Chinese patent application: based on the fusion of multiple similarities The drug similarity is calculated from the target protein and drug side effects data, and then weighted and summed to obtain t

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  • Drug-disease association prediction method and system
  • Drug-disease association prediction method and system

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Effect test

Embodiment 1

[0078] A drug-disease association prediction method, see figure 1 , including the following steps:

[0079] S1, obtaining a drug-disease association data set, the drug-disease association data set includes: a drug-disease association matrix, a drug chemical similarity matrix, and a disease semantic similarity matrix;

[0080] S2, performing a Gaussian kernel operation on the drug-disease correlation matrix to obtain a drug Gaussian kernel similarity matrix and a disease Gaussian kernel similarity matrix;

[0081] S3, combined with the chemical similarity matrix of the drug and the semantic similarity matrix of the disease, using the K nearest neighbor algorithm to reconstruct the drug-disease correlation matrix to obtain a drug-disease correlation reconstruction matrix;

[0082] S4, according to the drug-disease correlation reconstruction matrix, use the linear neighborhood similarity algorithm to calculate and obtain the drug linear neighborhood similarity matrix and the dis...

Embodiment 2

[0163] A drug-disease association prediction system, see Figure 5 , including a data set acquisition module 1, a Gaussian kernel operation module 2, an association matrix reconstruction module 3, a linear neighborhood similarity operation module 4, a matrix integration module 5, and a diffusion module 6; the data set acquisition module 1 is connected to the Gaussian Kernel operation module 2 and correlation matrix reconstruction module 3; Described Gauss kernel operation module 2 connects described matrix integration module 5; Described linear neighborhood similarity calculation module 4 connects described correlation matrix reconstruction module 3 and matrix integration module 5; the matrix integration module 5 is connected to the diffusion module 6; wherein:

[0164] The data set acquisition module 1 is used to obtain a drug-disease association data set, and the drug-disease association data set includes: a drug-disease association matrix, a medicinal chemical similarity ma...

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Abstract

Aiming at the limitation of the existing drug relocation technology, the invention provides a drug-disease association prediction method and system, and the method partially reduces the sparsity of an existing drug-disease original association matrix by applying a weight K nearest neighbor algorithm; meanwhile, a linear feature extraction mode and a nonlinear feature extraction mode are integrated when the similarity feature information of the medicine and the disease is extracted, so that more comprehensive similar feature information is obtained, and the relationship between the medicine and the disease can be fully reflected; and a bipartite graph diffusion method is adopted to calculate a drug-disease associated prediction score, so that excellent prediction performance is obtained.

Description

technical field [0001] The invention relates to the technical field of drug repositioning, in particular to a drug-disease association prediction method and system based on integrated similarity and bipartite graph diffusion algorithm. Background technique [0002] Drug repurposing aims to identify new indications for existing drugs, which will greatly reduce the cost and time of drug development. In recent years, researchers have explored a variety of computational methods for mining the link between existing drugs and diseases, including methods based on machine learning methods, matrix factorization, networks and graphs. However, all the methods applied to drug repositioning so far directly use the information of the unprocessed original drug-disease association matrix. Since only a small part of these similar association information has been proved and recorded in actual situations, there is still There is a lot of similar correlation information that needs to be proven...

Claims

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Application Information

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IPC IPC(8): G16H70/40
CPCG16H70/40
Inventor 顾国生李健明孙宇平林志毅谢国波
Owner GUANGDONG UNIV OF TECH
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