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A drug identification method based on biomedical knowledge graph reasoning

A biomedical and knowledge graph technology, applied in the field of data mining, can solve problems such as text mining methods

Active Publication Date: 2021-03-02
DALIAN UNIV OF TECH
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Problems solved by technology

[0007] However, with the rapid development of the biomedical field in recent years, the amount of biomedical literature has increased exponentially, and the massive amount of literature and information has brought difficulties to existing text mining methods.

Method used

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  • A drug identification method based on biomedical knowledge graph reasoning
  • A drug identification method based on biomedical knowledge graph reasoning
  • A drug identification method based on biomedical knowledge graph reasoning

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Embodiment Construction

[0039] The present invention is described below in conjunction with accompanying drawing and specific embodiment:

[0040] figure 1 It is an overall flowchart of a drug identification method based on biomedical knowledge graph reasoning in the present invention. A drug identification method based on biomedical knowledge map reasoning, comprising the following steps:

[0041] S1. Download biomedical text data: use the medical literature retrieval system PubMed to download biomedical literature in the form of time retrieval, and store the full text of the downloaded biomedical literature locally in the form of strings to obtain a biomedical literature database;

[0042] S2. Constructing a biomedical knowledge map: including the following steps:

[0043] a1. Extract the relationship between entities: use the relationship extraction tool SemRep to extract the relationship between biological entities from the biomedical literature database, and store the extracted relationship be...

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Abstract

A drug identification method based on biomedical knowledge map reasoning, the method includes the following steps: S1, downloading biomedical text data; S2, constructing a biomedical knowledge map; S3, constructing a drug-target-disease relationship data set; S4, using The graph embedding method performs representation learning on the graph; S5, the step of training the drug discovery model based on the long-short memory neural network; S6, the step of using the trained model for drug identification. The present invention is suitable for finding potential therapeutic drugs for diseases, and is not limited to the types of diseases and drugs; the drug for treating diseases can be effectively found from literature, which is of great significance to the field of drug discovery.

Description

technical field [0001] The invention relates to the field of data mining methods, in particular to a drug identification method based on biomedical knowledge map reasoning. Background technique [0002] Drug discovery is the core driving force for the development of the pharmaceutical industry and an important demand for social development. At present, there are mainly two types of drug discovery methods, namely high-throughput screening (High-throughput screening, HTS) and computer-aided drug discovery / design (computer-aided drug discovery / design, CADD). However, despite great innovations in drug R&D models and technologies, drug discovery is still a very long and costly process. It takes an average of 14 years to develop a new drug and costs about US$1.8 billion. Therefore, how to improve the efficiency of drug discovery has great theoretical and practical value. [0003] Discovering new drugs from the published biomedical literature is an economical and safe drug discov...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/36G06N3/08G16H20/10
CPCG06N3/08G16H20/10
Inventor 杨志豪桑盛田
Owner DALIAN UNIV OF TECH
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