School of Electronic Engineering, Huaihai Institute of Technology, Lianyungang, Jiangsu, China ; School of Information Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
School of Information Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
PLoS One. 2013 Dec 23;8(12):e83263. doi: 10.1371/journal.pone.0083263. eCollection 2013.
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.
利用遗传扰动数据和基因表达数据来推断调控网络是一种有效的策略，旨在提高基因间调控关系检测的准确性。基于这两种类型的数据，可以通过结构方程建模（SEM）准确地对遗传调控网络进行建模。本文基于 SEM 构建了线性回归（LR）模型，并提出了一种使用贝叶斯推断的新的迭代方案来估计 LR 模型的参数（LRBI）。还对 LRBI 与其他两种算法（基于自适应套索的算法（AL-Based）和稀疏最大似然（SML））进行了比较评估。模拟结果表明，LRBI 的性能明显优于 AL-Based，并且在检测功效方面优于 SML。将 LRBI 算法应用于实验数据，我们推断了 35 个酵母基因网络中的相互作用。LRBI 算法的开源程序可根据要求免费提供。
