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 journalArticle

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-.

Original languageEnglish
JournalCommunications in Statistics: Simulation and Computation
DOIs
Publication statusPublished - 2019 Jan 1

Fingerprint

Lasso
Gene Networks
Directed Acyclic Graph
Genes
Distinct
Breast Cancer
Therapy
Pathway
Cancer
Simulation Study
Gene
Target
Family

Keywords

  • 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

Cite this

Estimation of joint directed acyclic graphs with lasso family for gene networks. / Han, Sung Won; Park, Sunghoon; Zhong, Hua; Ryu, Eun Seok; Wang, Pei; Jung, Sehee; Lim, Jayeon; Yoon, Jeewhan; Kim, Sung Hwan.

In: Communications in Statistics: Simulation and Computation, 01.01.2019.

Research output: Contribution to journalArticle

Han, Sung Won ; Park, Sunghoon ; Zhong, Hua ; Ryu, Eun Seok ; Wang, Pei ; Jung, Sehee ; Lim, Jayeon ; Yoon, Jeewhan ; Kim, Sung Hwan. / Estimation of joint directed acyclic graphs with lasso family for gene networks. In: Communications in Statistics: Simulation and Computation. 2019.
@article{ab433d91124c4c51b95abb85b65b646c,
title = "Estimation of joint directed acyclic graphs with lasso family for gene networks",
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-.",
keywords = "Bayesian network, Drug response network, Lasso estimation, Probabilistic graphical model, Structure equation model, Unknown natural ordering",
author = "Han, {Sung Won} and Sunghoon Park and Hua Zhong and Ryu, {Eun Seok} and Pei Wang and Sehee Jung and Jayeon Lim and Jeewhan Yoon and Kim, {Sung Hwan}",
year = "2019",
month = "1",
day = "1",
doi = "10.1080/03610918.2019.1618869",
language = "English",
journal = "Communications in Statistics Part B: Simulation and Computation",
issn = "0361-0918",
publisher = "Taylor and Francis Ltd.",

}

TY - JOUR

T1 - Estimation of joint directed acyclic graphs with lasso family for gene networks

AU - Han, Sung Won

AU - Park, Sunghoon

AU - Zhong, Hua

AU - Ryu, Eun Seok

AU - Wang, Pei

AU - Jung, Sehee

AU - Lim, Jayeon

AU - Yoon, Jeewhan

AU - Kim, Sung Hwan

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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-.

AB - 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-.

KW - Bayesian network

KW - Drug response network

KW - Lasso estimation

KW - Probabilistic graphical model

KW - Structure equation model

KW - Unknown natural ordering

UR - http://www.scopus.com/inward/record.url?scp=85067571934&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85067571934&partnerID=8YFLogxK

U2 - 10.1080/03610918.2019.1618869

DO - 10.1080/03610918.2019.1618869

M3 - Article

AN - SCOPUS:85067571934

JO - Communications in Statistics Part B: Simulation and Computation

JF - Communications in Statistics Part B: Simulation and Computation

SN - 0361-0918

ER -