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Title: Inference of gene regulatory networks from genetic perturbations with linear regression model
Authors: Dong, Z
Song, T
Yuan C 
Issue Date: 2013
Publisher: Public Library of Science
Source: PLoS one, 2013, v. 8, no. 12, e83263, p. 1-9 How to cite?
Journal: PLoS one 
Abstract: It is an effective strategy to use both genetic perturbation data and gene expression data to infer regulatory networks that aims to improve the detection accuracy of the regulatory relationships among genes. Based on both types of data, the genetic regulatory networks can be accurately modeled by Structural Equation Modeling (SEM). In this paper, a linear regression (LR) model is formulated based on the SEM, and a novel iterative scheme using Bayesian inference is proposed to estimate the parameters of the LR model (LRBI). Comparative evaluations of LRBI with other two algorithms, the Adaptive Lasso (AL-Based) and the Sparsity-aware Maximum Likelihood (SML), are also presented. Simulations show that LRBI has significantly better performance than AL-Based, and overperforms SML in terms of power of detection. Applying the LRBI algorithm to experimental data, we inferred the interactions in a network of 35 yeast genes. An open-source program of the LRBI algorithm is freely available upon request.
EISSN: 1932-6203
DOI: 10.1371/journal.pone.0083263
Rights: © 2013 Dong et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The following publication Dong, Z., Song, T., & Yuan, C. (2013). Inference of gene regulatory networks from genetic perturbations with linear regression model. PLoS ONE, 8(12), e83263, 1-9 is available at
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