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

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 (PCA-CMI) is a well-known method in this field. In this study, we introduce a new alg...

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Vydáno v:PloS one Ročník 9; číslo 4; s. e92600
Hlavní autoři: Aghdam, Rosa, Ganjali, Mojtaba, Eslahchi, Changiz
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 01.04.2014
Public Library of Science (PLoS)
Témata:
Algorithms
Analysis
Artificial intelligence
Bayesian analysis
Bayesian statistical decision theory
Bioinformatics
Biology and Life Sciences
Computational Biology
Computer and Information Sciences
Computer science
Datasets
Gene expression
Gene Regulatory Networks - genetics
Genetic regulation
Genetic research
Parents & parenting
Physical Sciences
Probability distribution
Reverse engineering
Systems Biology
Values
Iran
ISSN:1932-6203, 1932-6203
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Shrnutí: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 (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 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 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 (n = 10, 50, 100) 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.
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Competing Interests: The authors have declared that no competing interests exist.
Wrote the paper: RA MG CE.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0092600