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 |
|---|---|
| Journal | Communications in Statistics: Simulation and Computation |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
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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 journal › Article
}
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 -