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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (7): 87-98    DOI: 10.11925/infotech.2096-3467.2021.1364
Original article Current Issue | Archive | Adv Search |
Building Multi-Source Semantic Knowledge Graph for Drug Repositioning
Zhang Han(),An Xinyu,Liu Chunhe
School of Health Management, China Medical University, Shenyang 110122, China
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Abstract  

[Objective] This paper constructs a cross-platform semantic knowledge graph with whole datasets, which helps us find novel drug knowledge. [Methods] First, we developed a new model for the proposed knowledge graph, which integrated semantic relations from PubMed, DrugBank and CTD, as well as knowledge fusion and attribute definition. Then, we conducted drug repositioning with pathway identification and link predication to discover new treatments for cancers. [Results] The F-score of pathway identification (0.57) was better than that of the linkage predication (0.56). The more pathways existing between drugs and indications, the greater possibility of predicting positively. [Limitations] Since the reasoning mechanism was based on the existing associations among knowledge units, it is hard to discover the novel indications for drugs without the known targets. It is difficult to update knowledge graph dynamically due to the huge data volume. [Conclusions] The proposed knowledge graph could effectively find new drug indications as well as improve the efficiency for drug research and development.
Key words： Knowledge Discovery      Drug Repositioning      Semantic Model      Knowledge Graph     
Received: 01 December 2021      Published: 24 August 2022
ZTFLH:  R9 G353  
Fund:Social Science Fund of Liaoning Province(L19BTQ006)
Corresponding Authors: Zhang Han,ORCID:0000-0002-9085-5875     E-mail: zhanghan@cmu.edu.cn
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Han Zhang
Xinyu An
Chunhe Liu

Cite this article:

Zhang Han, An Xinyu, Liu Chunhe. Building Multi-Source Semantic Knowledge Graph for Drug Repositioning. Data Analysis and Knowledge Discovery, 2022, 6(7): 87-98.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1364     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I7/87

Construction of Drug Knowledge Discovery Graph
分类 实体的语义类型
药物（Drug） orch, phsu, bacs, antb, elii, irda, imft, inch
靶点（Target） gngm, aapp
疾病（Disease） acab, anab, comd, agab, dsyn, emod, fndg,
inpo, mobd, neop, patf, sosy
Comparison of Category and Semantic Type for Entities in Knowledge Graph
Schematic Diagram of Knowledge Graph
药物 上市时间 最初用途 肿瘤治疗发现时间 发现途径 预测图谱 验证图谱
Itraconazole 1984 真菌感染 2007 小鼠模型 1984-2006 2007-2020
Nelfinavir 1997 HIV感染 2005 临床试验 1997-2004 2005-2020
Digoxin 1947 心脏病 1982 临床试验 1947-1981 1982-2020
Riluzole 1988 肌萎缩侧索硬化症 2009 小鼠模型 1988-2008 2009-2020
Overview of Drug Data
Pathways for Drug Discovery in Prediction Graph
方法 药物 预测数 标准数 预测正确数 准确率 召回率 F值
路径发现 Itraconazole 58 35 30 0.52 0.86 0.65
Nelfinavir 46 32 27 0.59 0.84 0.69
Digoxin 10 29 6 0.6 0.21 0.31
Riluzole 34 29 15 0.44 0.52 0.48
汇总 148 125 78 0.53 0.62 0.57
链路预测 Itraconazole 76 35 32 0.42 0.91 0.58
Nelfinavir 36 32 24 0.67 0.75 0.71
Digoxin 5 29 4 0.80 0.14 0.24
Riluzole 50 29 22 0.44 0.76 0.56
汇总 167 125 82 0.49 0.66 0.56
BITOLA Itraconazole 98 35 29 0.30 0.83 0.44
Nelfinavir 80 32 25 0.31 0.78 0.45
Digoxin 100 29 23 0.23 0.79 0.36
Riluzole 87 29 24 0.28 0.83 0.41
汇总 365 125 101 0.28 0.81 0.42
Predicting Results for Four Drugs
药物 LTC- Freq LTC-AMW Freq AMW Adamic Adar
Itraconazole 0.94 0.91 0.87 0.66 0.67
Nelfinavir 0.91 0.91 0.88 0.54 0.86
Digoxin 0.94 0.94 0.73 0.75 0.70
Riluzole 0.74 0.70 0.55 0.44 0.64
MAP of Four Ranking Metrics
Disease-Target Distributions for Positive and Negative Predicting Groups
Results for Itraconazole Discovery
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