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A Posterior Probability Approach for Gene Regulatory Network Inference in Genetic Perturbation Data
Mathematical Biosciences and Engineering
  • William Chad Young
  • Ka Yee Yeung, University of Washington Tacoma
  • Adrian E. Raftery
Publication Date
3-15-2016
Document Type
Article
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.

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Citation Information
William Chad Young, Ka Yee Yeung and Adrian E. Raftery. "A Posterior Probability Approach for Gene Regulatory Network Inference in Genetic Perturbation Data" Mathematical Biosciences and Engineering (2016)
Available at: http://works.bepress.com/ky-yeung/3/