PCA-CMI: Path Consistency Algorithm based on Conditional Mutual Information (for Gene Regulatory Network Reconstruction)

• Last updated: Jan. 18, 2012

Introduction#

Welcome to our website! This is a supporting webpage for our paper "Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information". We present a novel method for inferring gene regulatory network from gene expression data considering the nonlinear dependence and topological structure of gene regulations by employing path consistency algorithm (PCA) based on conditional mutual information (CMI). In this algorithm, the conditional dependence between a pair of genes is represented by the CMI between them. With the general hypothesis of Gaussian distribution underlying gene expression data, CMI between a pair of genes is computed by a concise formula involving the covariance matrices of the related gene expression profiles.

Any questions, please direct your mail to Zhi-Ping Liu - zpliu(AT)sibs.ac.cn.

(Note on Sept. 1, 2013: I have moved to Shandong University. My new email address is zpliu(AT)sdu.edu.cn)

Pay attention to the users: You can use and redistribute the data and code if you accept GNU General Public License (GPL).

Reference#

• Xiujun Zhang, Xing-Ming Zhao, Kun He, Le Lu, Yongwei Cao, Jingdong Liu, Jin-Kao Hao, Zhi-Ping Liu*, Luonan Chen*. Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information. Bioinformatics, 28(1):98-104, 2012.

Software and Data:#

• PCACMI.zip