Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Analyzing time series gene expression data
Bioinformatics
Generating Synthetic Gene Regulatory Networks
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
A framework for path analysis in gene regulatory networks
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Inference of Biological S-System Using the Separable Estimation Method and the Genetic Algorithm
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
mDBN: motif based learning of gene regulatory networks using dynamic bayesian networks
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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An efficient two-step Markov blanket method for modeling and inferring complex regulatory networks from large-scale microarray data sets is presented. The inferred gene regulatory network (GRN) is based on the time series gene expression data capturing the underlying gene interactions. For constructing a highly accurate GRN, the proposed method performs: 1) discovery of a gene's Markov Blanket (MB), 2) formulation of a flexible measure to determine the network's quality, 3) efficient searching with the aid of a guided genetic algorithm, and 4) pruning to obtain a minimal set of correct interactions. Investigations are carried out using both synthetic as well as yeast cell cycle gene expression data sets. The realistic synthetic data sets validate the robustness of the method by varying topology, sample size, time delay, noise, vertex in-degree, and the presence of hidden nodes. It is shown that the proposed approach has excellent inferential capabilities and high accuracy even in the presence of noise. The gene network inferred from yeast cell cycle data is investigated for its biological relevance using well-known interactions, sequence analysis, motif patterns, and GO data. Further, novel interactions are predicted for the unknown genes of the network and their influence on other genes is also discussed.