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Drug repositioning method based on multi-information fusion and random walk model

A random walk model and multi-information technology, applied in the field of bioinformatics, can solve problems such as no integration, no effective use of information diffusion, etc.

Active Publication Date: 2017-12-22
CENT SOUTH UNIV
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AI Technical Summary

Problems solved by technology

However, compared with the currently available biological information, the research on how to integrate and construct a multi-source biological information network and make effective predictions is still in its infancy
For the TL_HGBI method, this method does not integrate the experimentally verified disease-gene association information; while DrugNet completes the direct or indirect diffusion from the drug network to the disease network, but does not effectively use the information diffusion from the disease network to the drug network

Method used

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  • Drug repositioning method based on multi-information fusion and random walk model
  • Drug repositioning method based on multi-information fusion and random walk model
  • Drug repositioning method based on multi-information fusion and random walk model

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Embodiment Construction

[0063] Such as figure 1 Shown, the concrete realization process of the present invention is as follows:

[0064] 1. Calculate the similarity of diseases, drugs and targets, and construct drug-disease heterogeneous network disease-target-drug heterogeneous network;

[0065] The data sets used in this method include disease sets, drug sets, target sets, disease-drug association data, disease-target association data, and drug-target association data.

[0066] First, calculate disease, drug, and target similarities:

[0067] 1. Drug similarity calculation

[0068] Based on the SMILES chemical structure information of drugs, CDK (Chemical development kit) is used to calculate the chemical structure similarity between drugs, also known as molecular similarity. According to the similarity of all drug pairs, a drug similarity matrix is ​​constructed.

[0069] 2. Disease similarity calculation

[0070] The disease similarity is calculated by the tool MinMiner, which calculates the...

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Abstract

The invention discloses a drug repositioning method based on multi-information fusion and a random walk model. According to the method, disease-target-drug heterogeneous network is constructed through integrating existing disease data, drug data, target data, disease-drug associated data, disease-gene associated data and drug-target associated data; the basic random walk model is extended to the constructed heterogeneous network; and candidate therapeutic drugs are recommended for diseases through effectively utilizing global network information. The method disclosed by the invention is simple and effective; and compared with other methods and proved by tests on a standard data set, the method has good prediction performance in the aspect of drug repositioning.

Description

technical field [0001] The invention relates to the field of bioinformatics, in particular to a drug repositioning method based on multivariate information fusion and a random walk model to recommend candidate therapeutic drugs for diseases. Background technique [0002] Currently, the number of new drugs approved by the US Food and Drug Administration (FDA) each year is small, despite growing investment in drug research and development. New drug research and development is still a long cycle, huge cost, and there are high risks and low success rate. Statistics show that it takes about 15 years for a new drug to be developed and launched, costing more than US$800 million. At present, many pharmaceutical companies are trying to increase the speed of developing new drugs through innovative technologies such as computer molecular aided design, high-throughput screening, and combinatorial chemistry, but sales are still far below the cost of new drug research and development. I...

Claims

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Application Information

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IPC IPC(8): G06F19/00
CPCG16C20/50
Inventor 王建新罗慧敏李敏蒋辉卢诚谦
Owner CENT SOUTH UNIV
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