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

doi:10.1080/03610918.2019.1618869

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

School of Industrial Management Engineering

Research output: Contribution to journal › Article › peer-review

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 language	English
Pages (from-to)	2793-2807
Number of pages	15
Journal	Communications in Statistics: Simulation and Computation
Volume	50
Issue number	9
DOIs	https://doi.org/10.1080/03610918.2019.1618869
Publication status	Published - 2021

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1080/03610918.2019.1618869

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, Vol. 50, No. 9, 2021, p. 2793-2807.

Research output: Contribution to journal › Article › peer-review

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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}",

note = "Funding Information: This research was supported by grants from the National Research Foundation of Korea (NRF-2017R1E1A1A03070507) and Korea University (K1719881, K1822881). Please send any inquiry to the corresponding authors, Jeewhan Yoon (Department of Management of Technology, Graduate School of Management of Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, South Korea. Email: towny@korea.ac.kr), and SungHwan Kim (Department of Applied Statistics, Konkuk University, Gwangjin-gu, Seoul, Republic of Korea. Email: swiss747@gmail.com). Publisher Copyright: {\textcopyright} 2019 Taylor & Francis Group, LLC.",

year = "2021",

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AU - Park, Sunghoon

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AU - Ryu, Eun Seok

AU - Wang, Pei

AU - Jung, Sehee

AU - Lim, Jayeon

AU - Yoon, Jeewhan

AU - Kim, Sung Hwan

N1 - Funding Information: This research was supported by grants from the National Research Foundation of Korea (NRF-2017R1E1A1A03070507) and Korea University (K1719881, K1822881). Please send any inquiry to the corresponding authors, Jeewhan Yoon (Department of Management of Technology, Graduate School of Management of Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, South Korea. Email: towny@korea.ac.kr), and SungHwan Kim (Department of Applied Statistics, Konkuk University, Gwangjin-gu, Seoul, Republic of Korea. Email: swiss747@gmail.com). Publisher Copyright: © 2019 Taylor & Francis Group, LLC.

PY - 2021

Y1 - 2021

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-.

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KW - Bayesian network

KW - Drug response network

KW - Lasso estimation

KW - Probabilistic graphical model

KW - Structure equation model

KW - Unknown natural ordering

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ER -

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

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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