微重力生物知识图的构建及其在抗骨质疏松症药物预测中的应用
Yu-Han Zheng1, Guan-Jing Pan1, Yuan Quan1
1Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
Life sciences in space research
|April 26, 2024
概括
这项研究开发了一个知识图表,以识别微重力诱导的骨损失的药物. 该方法成功预测了潜在的骨质疏松症治疗方法,验证了其在太空医学药物发现中的应用.
科学领域:
- 太空医学 太空医学
- 药理学 药理学是指药理学的学科.
- 生物信息学是一种生物信息学.
背景情况:
- 太空探索增加了骨损失的风险.
- 宇航员需要有效的骨质疏松症药物.
- 传统的太空实验是昂贵和有限的.
研究的目的:
- 为微重力环境识别潜在的抗骨质疏松药物.
- 为了利用in-silico方法用于太空药物发现.
- 为微重力和骨质损失建立一个全面的知识图.
主要方法:
- 收集了184,915篇关于微重力和骨质损失的文学文章.
- 用于数据挖掘的依赖路径提取和聚类.
- 来自多个生物医学数据库的综合数据.
- 构建了微重力生物学知识图 (MBKG).
- 应用TransE模型用于知识图嵌入和药物预测.
主要成果:
- 在MBKG中包含134,796个实体和3,395,273个三胞胎.
- 预测的药物显示出高效率:7/10顶级西方药物被批准的骨质疏松症药物.
- 5/10顶级 传统中医有文献支持骨质损失治疗.
- 15/20预测的药物用于微重力骨损失在相关环境中进行了研究.
结论:
- 微重力生物学知识图 (MBKG) 是用于太空药物发现的宝贵工具.
- 体内方法可以有效地识别微重力诱导的骨损失的潜在治疗方法.
- 这种方法加快了对太空飞行相关健康问题的对策的开发.
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