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Estimation of joint directed acyclic graphs with lasso family for gene networks

Authors
Han, Sung WonPark, SunghoonZhong, HuaRyu, Eun-SeokWang, PeiJung, SeheeLim, JayeonYoon, JeewhanKim, SungHwan
Issue Date
2021
Publisher
TAYLOR & FRANCIS INC
Keywords
Bayesian network; Drug response network; Lasso estimation; Probabilistic graphical model; Structure equation model; Unknown natural ordering
Citation
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v.50, no.9, pp.2793 - 2807
Indexed
SCIE
SCOPUS
Journal Title
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Volume
50
Number
9
Start Page
2793
End Page
2807
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/144851
DOI
10.1080/03610918.2019.1618869
ISSN
0361-0918
Abstract
Biological regulatory pathways provide important information for target gene cancer therapy. Frequently, estimating the gene networks of two distinct patient groups is a worthwhile investigation. This paper proposes an approach, called jDAG, to the estimation of directed joint networks. It can identify common directed edges with joint data sets and distinct edges. In a simulation study, we show that the proposed jDAG outperforms existing methods although it does require longer computational times. We also present and discuss the example study of a breast cancer data set with ER + and ER-.
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