网络嵌入揭示了哺乳动物病毒组中隐藏的相互作用
Timothée Poisot1, Marie-Andrée Ouellet1, Nardus Mollentze2,3
1Département de Sciences Biologiques, Université de Montréal, Montréal, QC, Canada.
Patterns (New York, N.Y.)
|July 6, 2023
概括
使用网络科学预测哺乳动物与病毒的相互作用,揭示了全球病毒组的不充分特征. 优先考虑亚马逊盆地和撒哈拉以南非洲的发现,可以提高对疾病出现的理解.
科学领域:
- 网络科学 网络科学
- 病毒学 病毒学
- 计算生物学是一种计算生物学.
背景情况:
- 预测宿主病毒相互作用对于理解疾病的出现至关重要.
- 现有的方法经常与交互数据的复杂性和偏差作斗争.
研究的目的:
- 开发和验证一种新的基于网络的方法,用于预测宿主病毒相互作用.
- 确定具有新型病毒发现潜力较高的地区和水库.
- 通过基因组和网络数据改进对动物传染病威胁的预测.
主要方法:
- 开发了一种双边网络预测方法,将推系统 (线性过) 与低级图形嵌入相结合.
- 将该方法应用于全球哺乳动物-病毒相互作用数据库.
- 在指定的网络上利用图形嵌入来从病毒基因组特征预测人类感染风险.
主要成果:
- 该方法产生了生物可信和数据偏差强大的哺乳动物病毒相互作用的预测.
- 全球哺乳动物病毒组的特征显著不足.
- 确定亚马逊盆地和撒哈拉以南非洲为未来病毒发现的优先区域.
- 通过整合基因组和网络数据,改善了对人类感染风险的预测.
结论:
- 全球哺乳动物病毒网络结构包含大量可恢复的信息.
- 开发的方法为基本的病毒学和疾病出现提供了新的见解.
- 未来的监测和实验室研究应优先考虑确定的区域和病毒.
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