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) algorithm to infer the regulatory pathway from mixed observational data containing both continuous variables (e.g. expression of genes) and categorical variables (e.g. categorical phenotypes or single nucleotide polymorphisms). Our method can identify upstream causal factors and downstream effectors closely linked to a variable and generate hypotheses

SUBMITTER: Zhong W 

PROVIDER: S-EPMC7038820 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature