Large-Sample Learning of Bayesian Networks is NP-Hard
The Journal of Machine Learning Research
Learning Bayesian Networks
Exploring gene causal interactions using an enhanced constraint-based method
Pattern Recognition
A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n
The Journal of Machine Learning Research
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm
The Journal of Machine Learning Research
A comparison of novel and state-of-the-art polynomial Bayesian network learning algorithms
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
From alternative clustering to robust clustering and its application to gene expression data
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
The Benefit of Decomposing POMDP for Control of Gene Regulatory Networks
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Reverse engineering of gene regulatory networks from biological data
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
International Journal of Data Mining and Bioinformatics
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Constraint-based structure learning algorithms generally perform well on sparse graphs. Although sparsity is not uncommon, there are some domains where the underlying graph can have some dense regions; one of these domains is gene regulatory networks, which is the main motivation to undertake the study described in this paper. We propose a new constraint-based algorithm that can both increase the quality of output and decrease the computational requirements for learning the structure of gene regulatory networks. The algorithm is based on and extends the PC algorithm. Two different types of information are derived from the prior knowledge; one is the probability of existence of edges, and the other is the nodes that seem to be dependent on a large number of nodes compared to other nodes in the graph. Also a new method based on Gene Ontology for gene regulatory network validation is proposed. We demonstrate the applicability and effectiveness of the proposed algorithms on both synthetic and real data sets.