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BioMed Research International
Volume 2017, Article ID 6261802, 8 pages
Research Article

MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach

1Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA
2Computer Science Department, King Abdulaziz University, P.O. Box 80221, Jeddah 21589, Saudi Arabia
3Bioinformatics Program, New Jersey Institute of Technology, Newark, NJ 07102, USA
4Department of Biological Sciences, Rutgers University, Newark, NJ 07102, USA

Correspondence should be addressed to Turki Turki; as.ude.uak@ikrutt and Jason T. L. Wang; ude.tijn@jgnaw

Received 7 June 2016; Revised 14 November 2016; Accepted 13 December 2016; Published 22 January 2017

Academic Editor: Farit M. Afendi

Copyright © 2017 Yasser Abduallah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.