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The Five‐Gene‐Network Data Analysis with Local Causal Discovery Algorithm Using Causal Bayesian Networks
Author(s) -
Yoo Changwon,
Brilz Erik M.
Publication year - 2009
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1111/j.1749-6632.2008.03749.x
Subject(s) - bayesian network , pairwise comparison , causal model , bayesian probability , computer science , psychological intervention , causal inference , data mining , causal analysis , machine learning , gene regulatory network , causality (physics) , causal structure , microarray analysis techniques , computational biology , gene , artificial intelligence , mathematics , biology , econometrics , genetics , psychology , statistics , gene expression , physics , quantum mechanics , psychiatry
Using microarray experiments, we can model causal relationships of genes measured through mRNA expression levels. To this end, it is desirable to compare experiments of the system under complete interventions of some genes, such as by knock out of some genes, with experiments of the system under no interventions. However, it is expensive and difficult to conduct wet lab experiments of complete interventions of genes in a biological system. Thus, it will be helpful if we can discover promising causal relationships among genes with no interventions or incomplete interventions, such as by applying a treatment that has unknown effects to modeled genes, in order to identify promising genes to perturb in the system that can later be verified in wet laboratories. In this paper we use causal Bayesian networks to implement a causal discovery algorithm—the equivalence local implicit latent variable scoring method (EquLIM)—that identifies promising causal relationships even with a small dataset generated from no or incomplete interventions. We then apply EquLIM to analyze the five‐gene‐network data and compare EquLIM's predictions with true causal pairwise relationships between the genes.