Simple Bayesian Gene Network Learning in Populus Drought Transcriptome Data

Authors

  • Amir Almasi Zadeh Yaghuti Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Forest Genetics & Biotechnology, Ministry of Education, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China
  • Ali Movahedi Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Forest Genetics & Biotechnology, Ministry of Education, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China
  • Hui Wei Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Forest Genetics & Biotechnology, Ministry of Education, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China
  • Weibo Sun Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Forest Genetics & Biotechnology, Ministry of Education, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China
  • Mohaddeseh Mousavi Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Forest Genetics & Biotechnology, Ministry of Education, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China
  • Qiang Zhuge Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Forest Genetics & Biotechnology, Ministry of Education, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China

DOI:

https://doi.org/10.3329/bjb.v50i4.57075

Keywords:

Gene network, Populus, Microarray data, NCBI

Abstract

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)

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Published

2021-12-31

How to Cite

Zadeh Yaghuti, A. A. ., Movahedi, A. ., Wei, H. ., Sun, W. ., Mousavi, M. ., & Zhuge, Q. . (2021). Simple Bayesian Gene Network Learning in Populus Drought Transcriptome Data. Bangladesh Journal of Botany, 50(4), 1077–1086. https://doi.org/10.3329/bjb.v50i4.57075

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Articles