Estimation of joint directed acyclic graphs with lasso family for gene networks. Issue 9 (2nd September 2021)

• Record Type: Journal Article
• Authors: Han, Sung Won; Park, Sunghoon; Zhong, Hua; Ryu, Eun-Seok; Wang, Pei; Jung, Sehee; Lim, Jayeon; Yoon, Jeewhan; Kim, SungHwan
• Is Part Of: Communications in statistics. Volume 50:Issue 9(2021)
• Page Start: 2793
• Page End: 2807
• Publication Date: 2021-09-02
• Subjects: Bayesian network -- Drug response network -- Lasso estimation -- Probabilistic graphical model -- Structure equation model -- Unknown natural ordering; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2019.1618869
• Languages: English
• ISSNs: 0361-0918

Abstract

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-.