Open AccessSimple Bayesian Gene Network Learning in Populus Drought Transcriptome Data
Author(s) -
Amir Almasi Zadeh Yaghuti,
Ali Movahedi,
Hui Wei,
Weibo Sun,
Mohaddeseh Mousavi,
Qiang Zhuge
Publication year - 2021
Publication title -
bangladesh journal of botany
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.152
H-Index - 17
eISSN - 2079-9926
pISSN - 0253-5416
DOI - 10.3329/bjb.v50i4.57075
Subject(s) - gene regulatory network , bayesian network , inference , dynamic bayesian network , computational biology , bayesian probability , transcriptome , bayesian inference , computer science , machine learning , gene , artificial intelligence , data mining , systems biology , graphical model , microarray analysis techniques , biology , gene expression , genetics
Constructing a sensibly functional gene interaction network is highly appealing for better understanding system-level biological processes governing various Populus traits. Bayesian Network (BN) learning provides an elegant and compact statistical approach for modeling causal gene-gene relationships in microarray data. Therefore, it could come with the illumination of functional molecular playing in Biology Systems. In the present study, different forms of gene Bayesian networks were detected on Populus cellular transcriptome data. Markov blankets would likely be emerging at every possible gene regulatory Bayesian network level. Results showed that PtpAffx.1257.4.S1_a_at,1.0 hypothetical protein is the most important in its possible regulatory program. This paper illustrates that the gene network regulatory inference is possible to encapsulate within a single BN model. Therefore, such a BN model can serve as a promising training tool for Populus gene expression data for better future experimental scenarios.Bangladesh J. Bot. 50(4): 1077-1086, 2021 (December)