Fusion network drug target relationship prediction method based on network enhancement and graph regularization

A technology that integrates networks and prediction methods, applied in the field of systems biology, can solve the problems of low prediction accuracy, incomplete extraction of molecular information, and large noise in biological data, and achieve the effect of suppressing noise.

Active Publication Date: 2021-01-26
SUN YAT SEN UNIV
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Problems solved by technology

[0007] In order to overcome the defects of incomplete extraction of molecular information in the network drug target relationship prediction method in the prior art, and the large noise of biol

Method used

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  • Fusion network drug target relationship prediction method based on network enhancement and graph regularization
  • Fusion network drug target relationship prediction method based on network enhancement and graph regularization
  • Fusion network drug target relationship prediction method based on network enhancement and graph regularization

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Embodiment 1

[0056] Such as Figure 1-Figure 2 As shown, a fusion network drug target relationship prediction method based on network enhancement and graph regularization includes the following steps:

[0057] S1: Using the undirected graph model to model the drug similarity network and protein similarity network respectively;

[0058] More specifically, the drug similarity network includes: drug chemical structure similarity, drug-disease association similarity, drug-side effect similarity, drug interaction similarity; the protein similarity network includes: sequence structure similarity, protein- Disease association similarity, protein interaction similarity.

[0059] S2: Use the network enhancement method based on the third-order neighborhood random walk to enhance the modeled drug similarity network and protein similarity network;

[0060] The specific process includes:

[0061] S201: Both the drug similarity network and the protein similarity network are represented by G, N repres...

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Abstract

The invention discloses a fusion network drug target relationship prediction method based on network enhancement and graph regularization. The method comprises the following steps of modeling a drug similar network and a protein similar network by using an undirected graph model; performing enhancement processing on the modeled drug similar network and protein similar network by using a network enhancement method based on three-order neighborhood random walk; extracting the enhanced similar network by using a similar matrix decomposition model with graph regularities to respectively obtain a drug network feature representation and a protein network feature representation; and training the prediction model, and inputting the drug network feature representation and the feature representationvector of the protein network into the trained prediction model to obtain a prediction value of the association probability of the drug target pair. According to the method, the global connection relationship between molecules can be better captured, the noise can be effectively suppressed, and robustness is higher when the molecular network data with different scales and different noise degreesare used for prediction.

Description

technical field [0001] The invention relates to the technical field of systems biology, and more specifically, to a method for predicting drug-target relationship in a fusion network based on network enhancement and graph regularization. Background technique [0002] Drug target identification is an important method in modern drug development. With the accumulation of a large amount of omics data by high-throughput technology, the use of machine learning methods to fuse multiple information and find drugs or proteins with similar functions has become an important means of drug target identification. The starting point for identifying drug-target associations through drug or protein similarity is that similar drugs tend to act on similar targets, and similar target proteins are more likely to bind similar drugs. The fusion model can integrate different information such as the chemical structure of the drug, drug efficacy, drug-disease association, protein sequence structure,...

Claims

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Application Information

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IPC IPC(8): G16B15/30G06N3/04G06N3/08
CPCG16B15/30G06N3/08G06N3/047G06N3/045Y02A90/10
Inventor 张曦文戴道清王伟文任传贤
Owner SUN YAT SEN UNIV
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