Construction of gene regulatory networks using biclustering and Bayesian networks.
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.
University of Science and Technology, Sana'a, Yemen. fadlwork@gmail.com
Fadhl M
FM
Alakwaa
Nahed H
NH
Solouma
Yasser M
YM
Kadah
D000465:Algorithms; D001499:Bayes Theorem; D016000:Cluster Analysis; D030541:Databases, Genetic; D053263:Gene Regulatory Networks / Q000235:genetics*; D016014:Linear Models; D012372:ROC Curve; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics*