Inferring Regulatory Networks From Mixed Observational Data Using Directed Acyclic Graphs
Abstract
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 type of distribution, which prohibit a joint modeling of continuous gene expression and categorical variables. We present a new mixed DAG (mDAG)...
Paper Details
Title
Inferring Regulatory Networks From Mixed Observational Data Using Directed Acyclic Graphs
Published Date
Feb 7, 2020
Journal
Volume
11
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