Reference: Alakwaa FM, et al. (2011) Construction of gene regulatory networks using biclustering and Bayesian networks. Theor Biol Med Model 8:39

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Abstract


Background: Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to elucidate disease ontology fully and to reduce the cost of drug development, gene regulatory networks (GRNs) have to be constructed. During the last decade, many GRN inference algorithms based on genome-wide data have been developed to unravel the complexity of gene regulation. Time series transcriptomic data measured by genome-wide DNA microarrays are traditionally used for GRN modelling. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to the large number of genes. Dimensionality is one of the interesting problems in GRN modelling.

Results: In this paper, we develop a biclustering function enrichment analysis toolbox (BicAT-plus) to study the effect of biclustering in reducing data dimensions. The network generated from our system was validated via available interaction databases and was compared with previous methods. The results revealed the performance of our proposed method.

Conclusions: Because of the sparse nature of GRNs, the results of biclustering techniques differ significantly from those of previous methods.

Reference Type
Journal Article | Research Support, Non-U.S. Gov't
Authors
Alakwaa FM, Solouma NH, Kadah YM
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Gene Ontology Annotations


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Gene/Complex Qualifier Gene Ontology Term Aspect Annotation Extension Evidence Method Source Assigned On Reference

Phenotype Annotations


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Gene Phenotype Experiment Type Mutant Information Strain Background Chemical Details Reference

Disease Annotations


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Gene Disease Ontology Term Qualifier Evidence Method Source Assigned On Reference

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Regulator Target Direction Regulation Of Happens During Method Evidence

Post-translational Modifications


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Genetic Interactions

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Interactor Interactor Allele Assay Annotation Action Phenotype SGA score P-value Source Reference

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Gene Species Gene ID Strain background Direction Details Source Reference