CN: a consensus algorithm for inferring gene regulatory networks using the SORDER algorithm and conditional mutual information test

2015 ◽  
Vol 11 (3) ◽  
pp. 942-949 ◽  
Author(s):  
Rosa Aghdam ◽  
Mojtaba Ganjali ◽  
Xiujun Zhang ◽  
Changiz Eslahchi
Keyword(s):  
Mutual Information ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Consensus Algorithm ◽  
Conditional Mutual Information ◽  
Cn Algorithm ◽  
Gene Regulatory ◽  
Information Test

Diagram of the CN algorithm.

Inferring gene regulatory networks by PCA-CMI using Hill climbing algorithm based on MIT score and SORDER method

2016 ◽  
Vol 09 (03) ◽  
pp. 1650040
Author(s):  
Rosa Aghdam ◽  
Mohsen Alijanpour ◽  
Mehrdad Azadi ◽  
Ali Ebrahimi ◽  
Changiz Eslahchi ◽  
...  
Keyword(s):  
Mutual Information ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Hill Climbing ◽  
Directed Network ◽  
Conditional Mutual Information ◽  
Hill Climbing Algorithm ◽  
Pc Algorithm ◽  
Gene Regulatory ◽  
Information Test

Inferring gene regulatory networks (GRNs) is a challenging task in Bioinformatics. In this paper, an algorithm, PCHMS, is introduced to infer GRNs. This method applies the path consistency (PC) algorithm based on conditional mutual information test (PCA-CMI). In the PC-based algorithms the separator set is determined to detect the dependency between variables. The PCHMS algorithm attempts to select the set in the smart way. For this purpose, the edges of resulted skeleton are directed based on PC algorithm direction rule and mutual information test (MIT) score. Then the separator set is selected according to the directed network by considering a suitable sequential order of genes. The effectiveness of this method is benchmarked through several networks from the DREAM challenge and the widely used SOS DNA repair network of Escherichia coli. Results show that applying the PCHMS algorithm improves the precision of learning the structure of the GRNs in comparison with current popular approaches.

scholarly journals Part mutual information for quantifying direct associations in networks

2016 ◽  
Vol 113 (18) ◽  
pp. 5130-5135 ◽  
Author(s):  
Juan Zhao ◽  
Yiwei Zhou ◽  
Xiujun Zhang ◽  
Luonan Chen
Keyword(s):  
Mutual Information ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Partial Correlation ◽  
Simulated Data ◽  
Expression Data ◽  
Conditional Mutual Information ◽  
Science And Engineering ◽  
Nonlinear Associations ◽  
Gene Regulatory

Quantitatively identifying direct dependencies between variables is an important task in data analysis, in particular for reconstructing various types of networks and causal relations in science and engineering. One of the most widely used criteria is partial correlation, but it can only measure linearly direct association and miss nonlinear associations. However, based on conditional independence, conditional mutual information (CMI) is able to quantify nonlinearly direct relationships among variables from the observed data, superior to linear measures, but suffers from a serious problem of underestimation, in particular for those variables with tight associations in a network, which severely limits its applications. In this work, we propose a new concept, “partial independence,” with a new measure, “part mutual information” (PMI), which not only can overcome the problem of CMI but also retains the quantification properties of both mutual information (MI) and CMI. Specifically, we first defined PMI to measure nonlinearly direct dependencies between variables and then derived its relations with MI and CMI. Finally, we used a number of simulated data as benchmark examples to numerically demonstrate PMI features and further real gene expression data from Escherichia coli and yeast to reconstruct gene regulatory networks, which all validated the advantages of PMI for accurately quantifying nonlinearly direct associations in networks.

scholarly journals Parallel mutual information estimation for inferring gene regulatory networks on GPUs

2011 ◽  
Vol 4 (1) ◽  
Author(s):  
Haixiang Shi ◽  
Bertil Schmidt ◽  
Weiguo Liu ◽  
Wolfgang Müller-Wittig
Keyword(s):  
Mutual Information ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Gene Regulatory
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