High-order dynamic Bayesian Network learning with hidden common causes for causal gene regulatory network
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AbstractBackground: Inferring gene regulatory network (GRN) has been an important topic in Bioinformatics. Many computational methods infer the GRN from high-throughput expression data. Due to the presence of time delays in the regulatory relationships, High-Order Dynamic Bayesian Network (HO-DBN) is a good model of GRN. However, previous GRN inference methods assume causal sufficiency, i.e. no unobserved common cause. This assumption is convenient but unrealistic, because it is possible that relevant factors have not even been conceived of and therefore un-measured. Therefore an inference method that also handles hidden common cause(s) is highly desirable. Also, previous methods for discovering hidden common causes either do not handle multi-step time delays or restrict that the parents of hidden common causes are not observed genes.
All Author(s) ListLo LY, Wong ML, Lee KH, Leung KS
Journal nameBMC Bioinformatics
Volume Number16
LanguagesEnglish-United Kingdom
KeywordsCausality inference; Gene regulatory network; Hidden common cause; High-order dynamic Bayesian Network
Web of Science Subject CategoriesBiochemical Research Methods; Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology

Last updated on 2020-25-11 at 02:10