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