Differential network analysis: A statistical perspective

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. This article is categorized under: Data: Types and Structure > Graph and Network Data Statistical Models > Graphical Models

Automated summary

Network analysis is crucial in various scientific disciplines, especially in biology and medicine where it can predict complex diseases and provide insights into disease mechanisms. Recent statistical machine learning methods have been developed for inferring networks and identifying changes in their structures.