TY - JOUR
T1 - Joint Learning of Multiple Differential Networks with Latent Variables
AU - Ou-Yang, Le
AU - Zhang, Xiao Fei
AU - Zhao, Xing Ming
AU - Wang, Debby D.
AU - Wang, Fu Lee
AU - Lei, Baiying
AU - Yan, Hong
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - 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 under two different conditions is of great importance. However, most existing graphical models are developed for estimating a single graph and based on a tacit assumption that there is no missing relevant variables, which wastes the common information provided by multiple heterogeneous data sets and underestimates the influence of latent/unobserved relevant variables. In this paper, we propose a joint differential network analysis (JDNA) model to jointly estimate multiple differential networks with latent variables from multiple data sets. The JDNA model is built on a penalized D-trace loss function, with group lasso or generalized fused lasso penalties. We implement a proximal gradient-based alternating direction method of multipliers to tackle the corresponding convex optimization problems. Extensive simulation experiments demonstrate that JDNA model outperforms state-of-the-art methods in estimating the structural changes of graphical models. Moreover, a series of experiments on several real-world data sets have been performed and experiment results consistently show that our proposed JDNA model is effective in identifying differential networks under different conditions.
AB - 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 under two different conditions is of great importance. However, most existing graphical models are developed for estimating a single graph and based on a tacit assumption that there is no missing relevant variables, which wastes the common information provided by multiple heterogeneous data sets and underestimates the influence of latent/unobserved relevant variables. In this paper, we propose a joint differential network analysis (JDNA) model to jointly estimate multiple differential networks with latent variables from multiple data sets. The JDNA model is built on a penalized D-trace loss function, with group lasso or generalized fused lasso penalties. We implement a proximal gradient-based alternating direction method of multipliers to tackle the corresponding convex optimization problems. Extensive simulation experiments demonstrate that JDNA model outperforms state-of-the-art methods in estimating the structural changes of graphical models. Moreover, a series of experiments on several real-world data sets have been performed and experiment results consistently show that our proposed JDNA model is effective in identifying differential networks under different conditions.
KW - Differential network analysis
KW - fused lasso
KW - graphical model
KW - group lasso
KW - latent variable
UR - http://www.scopus.com/inward/record.url?scp=85049691759&partnerID=8YFLogxK
U2 - 10.1109/TCYB.2018.2845838
DO - 10.1109/TCYB.2018.2845838
M3 - Article
C2 - 29994625
AN - SCOPUS:85049691759
SN - 2168-2267
VL - 49
SP - 3494
EP - 3506
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 9
M1 - 8405747
ER -