TarKG:一个全面的生物医学知识图表用于目标发现
Cong Zhou1, Chui-Pu Cai2, Xiao-Tian Huang1
1Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu 610041, China.
Bioinformatics (Oxford, England)
|October 11, 2024
概括
我们开发了TarKG,这是一个全面的生物医学知识图表,用于药物向发现. TarKG集成多种数据源来预测潜在的治疗点和相关疾病,帮助药物开发.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 目标发现对于药物开发成功至关重要.
- 生物医学知识图 (KG) 擅长处理复杂的生物数据.
- 现有的KG仅限于他们专注于目标发现.
研究的目的:
- 构建一个专门的生物医学KG用于目标发现.
- 通过整合多个数据源来创建TarKG.
- 为了能够预测新的疾病-基因-化合物关系.
主要方法:
- 整合了七个生物医学KG,九个公共数据库和TCM知识.
- 开发了TarKG,拥有超过114万个实体和3280万个关系.
- 利用KG嵌入算法来完成知识和链接预测.
主要成果:
- TarKG有效地预测疾病目标链接.
- 案例研究成功地确定了阿尔茨海默病的潜在目标.
- TarKG确定了与CSN5蛋白相关的疾病.
结论:
- TarKG是加速药物标发现的宝贵资源.
- 知识图有助于预测新的治疗目标.
- 一个用户友好的Web服务器可用于知识检索和推理.
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