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Influence of Prior Knowledge in Constraint-Based Learning of Gene Regulatory Networks
January-February 2011 (vol. 8 no. 1)
pp. 130-142
Mehmet Tan, Middle East Technical University, Ankara and University of Calgary, Calgary
Mohammed Alshalalfa, University of Calgary, Calgary
Reda Alhajj, University of Calgary, Calgary and Global University, Beirut
Faruk Polat, Middle East Technical University, Ankara
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.

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Index Terms:
Gene regulatory networks, transcription factors, genes, microarray data, gene ontology, prior knowledge-based learning.
Citation:
Mehmet Tan, Mohammed Alshalalfa, Reda Alhajj, Faruk Polat, "Influence of Prior Knowledge in Constraint-Based Learning of Gene Regulatory Networks," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 1, pp. 130-142, Jan.-Feb. 2011, doi:10.1109/TCBB.2009.58
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