网基:微生物-代谢物网络重建和微生物利基分析
Lu Wang1,2, Lequn Wang2, Luonan Chen2,3
1Department of Bioinformatics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300203 People's Republic of China.
Phenomics (Cham, Switzerland)
|July 3, 2025
概括
NetNiche使用图形表示学习准确地预测了微生物-代谢物和微生物-微生物相互作用. 这种计算框架增强了对微生物组的多学科研究.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 甲基因组学和代谢学是研究微生物与代谢物相互作用的关键.
- 目前缺乏准确的计算方法来推断这些相互作用.
研究的目的:
- 介绍NetNiche,一个用于图形表示学习的新型上下文感知框架.
- 通过整合丰度数据和先前知识,准确预测微生物代谢物和微生物与微生物的相互作用.
主要方法:
- 开发了NetNiche,一个图形表示学习框架.
- 综合的微生物和代谢物丰度数据与先前的知识.
- 应用NetNiche对肠道和土壤微生物群数据集.
主要成果:
- 与SPIEC-EASI,SparCC和mmvec等最先进的方法相比,NetNiche的性能表现优越.
- 在多种微生物组数据集中成功预测了微生物代谢物和微生物与微生物的相互作用.
结论:
- NetNiche是一个有效的计算工具,用于预测微生物-代谢物和微生物-微生物相互作用.
- 该框架在多学科研究中具有广泛的适用性,特别是对人类微生物群的研究.
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