Incorporating prior information into differential network analysis using non‐paranormal graphical models
Sign Up to like & getrecommendations! Published in 2017 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btx208
Abstract: Motivation: Understanding how gene regulatory networks change under different cellular states is important for revealing insights into network dynamics. Gaussian graphical models, which assume that the data follow a joint normal distribution, have been used… read more here.
Keywords: information; differential networks; differential network; prior information ... See more keywords
Joint Learning of Multiple Differential Networks With Latent Variables
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Cybernetics"
DOI: 10.1109/tcyb.2018.2845838
Abstract: Graphical models have been widely used to learn the conditional dependence structures among random variables. In many controlled experiments, such as the studies of disease or drug effectiveness, learning the structural changes of graphical models… read more here.
Keywords: differential networks; graphical models; multiple differential; networks latent ... See more keywords