Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6897
Title: Influence of Prior Knowledge in Constraint-Based Learning of Gene Regulatory Networks
Authors: Tan, Mehmet
Alshalalfa, Mohammed
Alhajj, Reda
Polat, Faruk
Keywords: Gene regulatory networks
transcription factors
genes
microarray data
gene ontology
prior knowledge-based learning
Issue Date: 2011
Publisher: IEEE Computer Soc
Abstract: 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.
URI: https://doi.org/10.1109/TCBB.2009.58
https://hdl.handle.net/20.500.11851/6897
ISSN: 1545-5963
1557-9964
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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