论文标题
差分网络分析：统计观点
Differential Network Analysis: A Statistical Perspective
论文作者
论文摘要
网络有效地捕获了复杂系统组件之间的相互作用，因此已成为许多科学学科的中流tay柱。越来越多的证据，特别是来自生物学的证据表明，网络会随着时间的流逝而发生变化，并响应外部刺激。在生物学和医学中，已经发现这些变化可以预测复杂疾病。它们还被用来洞悉疾病开始和进展机制。本文主要是由生物学应用的动机，对最近的统计机器学习方法进行了回顾，该方法用于推断网络并确定其结构的变化。
Networks effectively capture interactions among components of complex systems, and have thus become a mainstay in many scientific disciplines. Growing evidence, especially from biology, suggest that networks undergo changes over time, and in response to external stimuli. In biology and medicine, these changes have been found to be predictive of complex diseases. They have also been used to gain insight into mechanisms of disease initiation and progression. Primarily motivated by biological applications, this article provides a review of recent statistical machine learning methods for inferring networks and identifying changes in their structures.
