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

Sung Won Han, Sunghoon Park, Hua Zhong, Eun Seok Ryu, Pei Wang, Sehee Jung, Jayeon Lim, Jeewhan Yoon, Sung Hwan Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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-.

Original languageEnglish
Pages (from-to)2793-2807
Number of pages15
JournalCommunications in Statistics: Simulation and Computation
Issue number9
Publication statusPublished - 2021

Bibliographical note

Publisher Copyright:
© 2019 Taylor & Francis Group, LLC.


  • Bayesian network
  • Drug response network
  • Lasso estimation
  • Probabilistic graphical model
  • Structure equation model
  • Unknown natural ordering

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation


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