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Paper IPM / Biological / 13260  


Abstract:  
Inferring gene regulatory networks (GRNs) is a major issue in systems biology, which explicitly characterizes regulatory processes in the cell. The Path Consistency Algorithm based on Conditional Mutual Information (PCACMI) is a wellknown method in this field. In this study, we introduce a new algorithm (IPCACMI) and apply it to a number of gene expression data sets in order to evaluate the accuracy of the algorithm to infer GRNs. The IPCACMI can be categorized as a hybrid method, using the PCACMI and HillClimbing algorithm (based on MIT score). The conditional dependence between variables is determined by the conditional mutual information test which can take into account both linear and nonlinear genes relations. IPCACMI uses a score and search method and defines a selected set of variables which is adjacent to one of X or Y. This set is used to determine the dependency between X and Y. This method is compared with the method of evaluating dependency by PCACMI in which the set of variables adjacent to both X and Y, is selected. The merits of the IPCACMI are evaluated by applying this algorithm to the DREAM3 Challenge data sets with n variables and n samples (n 10,50,100) and to experimental data from Escherichia coil containing 9 variables and 9 samples. Results indicate that applying the IPCACMI improves the precision of learning the structure of the GRNs in comparison with that of the PCACMI.
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