Inferring Regulatory Networks From Mixed Observational Data Using Directed Acyclic Graphs

Inferring Regulatory Networks From Mixed Observational Data Using Directed Acyclic Graphs

Construction of regulatory networks using cross-sectional expression profiling of genes is desired, but challenging. The Directed Acyclic Graph (DAG) provides a general framework to infer causal effects from observational data. However, most existing DAG methods assume that all nodes follow the same...

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Bibliographic Details
Main Authors: Wujuan Zhong, Li Dong, Taylor B. Poston, Toni Darville, Cassandra N. Spracklen, Di Wu, Karen L. Mohlke, Yun Li, Quefeng Li, Xiaojing Zheng
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-02-01
Series:Frontiers in Genetics
Subjects:
regulatory network
directed acyclic graphs
mixed observational data
continuous and categorical variables
causal regulatory pathways
Online Access:https://www.frontiersin.org/article/10.3389/fgene.2020.00008/full

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https://www.frontiersin.org/article/10.3389/fgene.2020.00008/full

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