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

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-.

Automated summary

Biological regulatory pathways are crucial for cancer therapy. This paper introduces a method called jDAG for estimating gene networks, which can identify common and distinct directed edges in joint data sets. Despite requiring longer computational times, the proposed jDAG outperforms existing methods in simulation and case studies of breast cancer data sets.