DAGBagM: Learning directed acyclic graphs of mixed variables with an application to identify prognostic protein biomarkers in ovarian cancer

Author:

Chowdhury Shrabanti,Wang Ru,Yu Qing,Huntoon Catherine J.,Karnitz Larry M.,Kaufmann Scott H.,Gygi Steven P.,Birrer Michael J.,Paulovich Amanda G.,Peng Jie,Wang Pei

Abstract

AbstractMotivationDirected gene/protein regulatory networks inferred by applying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of clinical outcomes. However, there remain unsolved challenges in DAG learning to jointly model clinical outcome variables, which often take binary values, and biomarker measurements, which usually are continuous variables. Therefore, in this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models for continuous and binary variables, DAGBagM allows for either type of nodes to be parents or children nodes in the learned graph. DAGBagM also employs a bootstrap aggregating strategy to reduce false positives and achieve better estimation accuracy. Moreover, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges for DAG reconstruction.ResultsAs shown by simulation studies, DAGBagM performs better in identifying edges between continuous and binary nodes, as compared to commonly used strategies of either treating binary variables as continuous or discretizing continuous variables. Moreover, DAGBagM outperforms several popular DAG structure learning algorithms including the score-based hill climbing (HC) algorithm, constraint-based PC-algorithm (PC-alg), and the hybrid method max-min hill climbing (MMHC) even for constructing DAG with only continuous nodes. The HC implementation in the R package DAGBagM is much faster than that in a widely used DAG learning R package bnlearn. When applying DAGBagM to proteomics datasets from ovarian cancer studies, we identify potential prognostic protein biomarkers in ovarian cancer.Availability and implementationDAGBagMis made available as a github repositoryhttps://github.com/jie108/dagbagM.

Publisher

Cold Spring Harbor Laboratory

Reference42 articles.

1. Scoring bayesian networks of mixed variables;International Journal of Data Science and Analytics,2018

2. An integrative analysis of tumor proteomic and phosphoproteomic profiles to examine the relationships between kinase activity and phosphorylation;Mol Cell Proteomics,2019

3. Asvatourian, V. , Coutzac, C. , Chaput, N. , Robert, C. , Michiels, S. , and Lanoy, E. (2018). Estimating causal effects of time-dependent exposures on a binary endpoint in a high-dimensional setting. BMC Medical Research Methodology, 18(67).

4. Bishop, C. et al. (2006). Pattern recognition and machine learning, volume 4. springer New York.

5. Boroughs, L. K. and DeBerardinis, R. J. (2016). Metabolic pathways promoting cancer cell survival and growth. Nature Cell Biology, 17(4).

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3