挖掘文献和途径数据以探索胺与神经递质和肠道微生物群的关系,使用知识图
Ting Liu1,2, K Anton Feenstra1, Zhisheng Huang2
1Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.
Bioinformatics (Oxford, England)
|December 26, 2023
概括
我们开发了KetPath,这是一个知识图,结合了手动策划和自动化方法来分析生物途径. 这种方法揭示了肠道微生物如何影响胺.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 系统生物学 系统生物学
背景情况:
- 生物通路的知识往往被封闭在出版物中,阻碍了计算分析.
- 为路径分析整合多样化的数据源带来了重大挑战.
研究的目的:
- 开发一种采矿生物通路知识图的方法.
- 为了研究肠道微生物群在胺胺的抗抑郁作用中的作用.
主要方法:
- 结合手动策划与自动命名实体识别和关系提取.
- 开发了"KetPath"知识图,整合了精选的事实,生物医学文本和公共数据库.
- 使用BioKetBERT工具进行自动关系提取.
主要成果:
- KetPath知识图表成功地整合了各种数据源.
- 自动提取识别了药物作用,途径关系和并发实体.
- 研究结果表明,肠道微生物可能通过BDNF表达增强胺胺的持续抗抑郁作用.
结论:
- 开发的方法有助于语义整合和生物途径的计算分析.
- KetPath提供了一个查询综合生物医学数据的框架.
- 这项研究有助于理解心理药理学中的微生物群-肠-大脑轴.
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