Vol 9: IPCA-CMI: An Algorithm for Inferring Gene Regulatory Networks based on a Combination of PCA-CMI and MIT Score.Report as inadecuate



 Vol 9: IPCA-CMI: An Algorithm for Inferring Gene Regulatory Networks based on a Combination of PCA-CMI and MIT Score.


Vol 9: IPCA-CMI: An Algorithm for Inferring Gene Regulatory Networks based on a Combination of PCA-CMI and MIT Score. - Download this document for free, or read online. Document in PDF available to download.

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This article is from PLoS ONE, volume 9.AbstractInferring 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 PCA-CMI is a well-known method in this field. In this study, we introduce a new algorithm IPCA-CMI and apply it to a number of gene expression data sets in order to evaluate the accuracy of the algorithm to infer GRNs. The IPCA-CMI can be categorized as a hybrid method, using the PCA-CMI and Hill-Climbing 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. IPCA-CMI uses a score and search method and defines a selected set of variables which is adjacent to one of 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 PCA-CMI in which the set of variables adjacent to both X and Y, is selected. The merits of the IPCA-CMI are evaluated by applying this algorithm to the DREAM3 Challenge data sets with n variables and n samples and to experimental data from Escherichia coil containing 9 variables and 9 samples. Results indicate that applying the IPCA-CMI improves the precision of learning the structure of the GRNs in comparison with that of the PCA-CMI.



Author: Aghdam, Rosa; Ganjali, Mojtaba; Eslahchi, Changiz

Source: https://archive.org/



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