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

William Chad  Young; Adrian E.  Raftery; Ka Yee  Yeung

doi:10.3934/mbe.2016041

Article Contents

2016, Volume 13, Issue 6: 1241-1251. Doi: 10.3934/mbe.2016041 open access

This issue Previous Article Optimal control of a mathematical model for cancer chemotherapy under tumor heterogeneity Next Article

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: September 2015

Revised: May 2016

Published: August 2016

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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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Mathematics Subject Classification: Primary: 62P10, 92D10; Secondary: 92C42.

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