论文标题
超越数据样本：将差分网络估算与科学知识保持一致
Beyond Data Samples: Aligning Differential Networks Estimation with Scientific Knowledge
论文作者
论文摘要
在两个现实生活中学习两种环境之间的差异统计依赖性网络至关重要，主要是在高维低样本制度中。在本文中，我们提出了一个新型的差异网络估计器，该估计器允许将各种知识来源整合到数据样本之外。所提出的估计器可扩展到大量变量，并达到尖锐的渐近收敛速率。关于广泛的模拟数据和四个现实世界应用（一个关于神经影像学的一个，三个来自功能基因组学）的经验实验表明，我们的方法实现了改进的差异网络估计，并为分类等下游任务提供了更好的支持。我们的结果凸显了在差异遗传网络识别和大脑连接组变化发现中整合组，空间和解剖学知识的重要好处。
Learning the differential statistical dependency network between two contexts is essential for many real-life applications, mostly in the high dimensional low sample regime. In this paper, we propose a novel differential network estimator that allows integrating various sources of knowledge beyond data samples. The proposed estimator is scalable to a large number of variables and achieves a sharp asymptotic convergence rate. Empirical experiments on extensive simulated data and four real-world applications (one on neuroimaging and three from functional genomics) show that our approach achieves improved differential network estimation and provides better supports to downstream tasks like classification. Our results highlight significant benefits of integrating group, spatial and anatomic knowledge during differential genetic network identification and brain connectome change discovery.
