English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Graphical models are widely used in scienti fic and engineering research torepresent conditional independence structures between random variables. In manycontrolled experiments, environmental changes or external stimuli can oftenalter the conditional dependence between the random variables, and potentiallyproduce significant structural changes in the corresponding graphical models.Therefore, it is of great importance to be able to detect such structuralchanges from data, so as to gain novel insights into where and how thestructural changes take place and help the system adapt to the new environment.Here we report an effective learning strategy to extract structural changes inGaussian graphical model using l1-regularization based convex optimization. Wediscuss the properties of the problem formulation and introduce an efficientimplementation by the block coordinate descent algorithm. We demonstrate theprinciple of the approach on a numerical simulation experiment, and we thenapply the algorithm to the modeling of gene regulatory networks under differentconditions and obtain promising yet biologically plausible results.

Learning Structural Changes of Gaussian Graphical Models in Controlled Experiments