Combining Multiple Types of Biological Data in Constraint-Based Learning of Gene Regulatory Networks

Tan, Mehmet
AlShalalfa, Mohammed
Alhajj, Reda
Polat, Faruk
Due to the complex structure and scale of gene regulatory networks, we support the argument that combination of multiple types of biological data to derive satisfactory network structures is necessary to understand the regulatory mechanisms of cellular systems. In this paper, we propose a simple but effective method of combining two types of biological data, namely microarray and transcription factor (TF) binding data, to construct gene regulatory networks. The proposed algorithm is based on and extends the well-known PC algorithm [23]. Further, we developed a method for measuring the significance of the interactions between the genes and the TFs. The reported test results on both synthetic and real data sets demonstrate the applicability and effectiveness of the proposed approach; we also report the results of some comparative analysis that highlights the power of the proposed approach.


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The glutathione-S-transferases (GSTs) (EC. are enzymes that participate in cellular detoxification of endogenous as well as foreign electrophilic compounds, function in the cellular detoxification systems and are evolved to protect cells against reactive oxygen metabolites by conjugating the reactive molecules to the nucleophile scavenging tripeptide glutathione (GSH, ?-glu-cys-gly). The GSTs are found in all eukaryotes and prokaryotic systems, in the cytoplasm, on the microsomes, and in the mitoch...
Employing decomposable partially observable Markov decision processes to control gene regulatory networks
Erdogdu, Utku; Polat, Faruk; Alhajj, Reda (2017-11-01)
Objective: Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs).
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Erdogdu, Utku; Polat, Faruk; Alhajj, Reda (2012-11-09)
Gene regulatory networks (GRNs) govern the protein transcription process in the cell and interactions among genes play a vital role in determining the biosynthesis rate of proteins. By using intervention techniques discovered by biological research it is possible to control a GRN, thus promoting or demoting the expression rate of a certain gene. In this work, this control task is studied in a partially observable setting where interventions lack perfect knowledge of the expression level of all genes. Moreov...
Citation Formats
M. Tan, M. AlShalalfa, R. Alhajj, and F. Polat, “Combining Multiple Types of Biological Data in Constraint-Based Learning of Gene Regulatory Networks,” 2008, Accessed: 00, 2020. [Online]. Available: