Reconstructing gene regulatory networks from time-series microarray data

2005 ◽  
Vol 350 (1) ◽  
pp. 63-69 ◽  
Author(s):  
S.P. Li ◽  
J.J. Tseng ◽  
S.C. Wang
Keyword(s):  
Time Series ◽  
Gene Regulatory Networks ◽  
Microarray Data ◽  
Regulatory Networks ◽  
Gene Regulatory ◽  
Time Series Microarray

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

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yasser Abduallah ◽  
Turki Turki ◽  
Kevin Byron ◽  
Zongxuan Du ◽  
Miguel Cervantes-Cervantes ◽  
...  
Keyword(s):  
Time Series ◽  
Gene Regulatory Networks ◽  
Microarray Data ◽  
Regulatory Networks ◽  
Experimental Results ◽  
Theoretic Approach ◽  
Information Theoretic ◽  
Gene Regulatory ◽  
Information Theoretic Approach ◽  
Time Series Microarray

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.

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