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Mathematical Biosciences and Engineering (MBE)
 

A posterior probability approach for gene regulatory network inference in genetic perturbation data

Pages: 1241 - 1251, Volume 13, Issue 6, December 2016      doi:10.3934/mbe.2016041

 
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William Chad Young - University of Washington, Department of Statistics, Box 354322, Seattle, WA 98195-4322, United States (email)
Adrian E. Raftery - University of Washington, Department of Statistics, Box 354322, Seattle, WA 98195-4322, United States (email)
Ka Yee Yeung - University of Washington, Institute of Technology, Box 358426, 1900 Commerce Street, Tacoma, WA 98402-3100, United States (email)

Abstract: 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.

Keywords:  Bayesian analysis, gene regulatory network, statistics, statistical computation.
Mathematics Subject Classification:  Primary: 62P10, 92D10; Secondary: 92C42.

Received: September 2015;      Accepted: May 2016;      Available Online: August 2016.

 References