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Mathematical Biosciences and Engineering

2016, Volume 13, Issue 6: 1241-1251. doi: 10.3934/mbe.2016041
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A posterior probability approach for gene regulatory network inference in genetic perturbation data

  • 1.
    University of Washington, Department of Statistics, Box 354322, Seattle, WA 98195-4322
  • 2.
    University of Washington, Institute of Technology, Box 358426, 1900 Commerce Street, Tacoma, WA 98402-3100
  • Received: 01 September 2015 Accepted: 29 June 2018 Published: 01 August 2016

  • MSC : Primary: 62P10, 92D10; Secondary: 92C42.

  • Inferring gene regulatory networks is an important problem in systems biology. However, these networks can be hard to infer from experimental data because of the inherent variability in biological data as well as the large number of genes involved. We propose a fast, simple method for inferring regulatory relationships between genes from knockdown experiments in the NIH LINCS dataset by calculating posterior probabilities, incorporating prior information. We show that the method is able to find previously identified edges from TRANSFAC and JASPAR and discuss the merits and limitations of this approach.

    Citation: William Chad Young, Adrian E. Raftery, Ka Yee Yeung. A posterior probability approach for gene regulatory network inference in genetic perturbation data[J]. Mathematical Biosciences and Engineering, 2016, 13(6): 1241-1251. doi: 10.3934/mbe.2016041

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  • Inferring gene regulatory networks is an important problem in systems biology. However, these networks can be hard to infer from experimental data because of the inherent variability in biological data as well as the large number of genes involved. We propose a fast, simple method for inferring regulatory relationships between genes from knockdown experiments in the NIH LINCS dataset by calculating posterior probabilities, incorporating prior information. We show that the method is able to find previously identified edges from TRANSFAC and JASPAR and discuss the merits and limitations of this approach.


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  • © 2016 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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