Building Multi-Source Semantic Knowledge Graph for Drug Repositioning

被引：0
|
作者：
Han Z. [1 ]
Xinyu A. [1 ]
Chunhe L. [1 ]
机构：
[1] School of Health Management, China Medical University, Shenyang
来源：
Data Analysis and Knowledge Discovery | 2022年 / 6卷 / 07期
关键词：
Drug Repositioning; Knowledge Discovery; Knowledge Graph; Semantic Model;
D O I：
10.11925/infotech.2096-3467.2021.1364
中图分类号：
学科分类号：
摘要：
[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. © 2022, Chinese Academy of Sciences. All rights reserved.
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页码：87 / 98
页数：11
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