从时间序列数据推断出多个原子网络的推断
María Moscardó García1, Atte Aalto1, Arthur N Montanari1,2,3
1Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.
NPJ systems biology and applications
|October 14, 2025
概括
我们开发了MINIE,一种新的计算方法,用于整合多原子数据,以理解复杂的生物网络. MINIE 改进了跨分子层和时间尺度的监管关系的推断.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 基因组学就是基因组学.
背景情况:
- 生物现象型是由于复杂的分子相互作用而产生的.
- 当前的数据驱动方法往往分析单一的omics,忽视跨层监管动态.
- 整合多原子数据对于全面了解细胞调节至关重要.
研究的目的:
- 开发一种计算方法,MINIE,用于整合多原子数据.
- 在不同的分子层和时间尺度上推断监管网络.
- 为了解决网络推理中单个omic方法的局限性.
主要方法:
- 开发了基于贝叶斯回归的计算方法MINIE.
- 集成的批量代谢学和单细胞转录学数据.
- 模拟了分子层之间的时间尺度分离,以改善推理.
主要成果:
- MINIE在欧米层和内部展示了准确和强大的预测性能.
- 在模拟数据集和帕金森病实验数据上得到验证.
- 在多原子网络推断中超越了最先进的方法.
结论:
- MINIE为全面的多原子网络推断提供了一个强大的工具.
- 该方法有效地整合了分子层和时间尺度上的监管动态.
- 通过综合的奥米克分析,可以更深入地了解复杂的生物系统.
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