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MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach
Joint Authors
Abduallah, Yasser
Turki, Turki
Byron, Kevin
Du, Zongxuan
Cervantes-Cervantes, Miguel
Wang, Jason T. L.
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-01-22
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Abduallah, Yasser& Turki, Turki& Byron, Kevin& Du, Zongxuan& Cervantes-Cervantes, Miguel& Wang, Jason T. L.. 2017. MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach. BioMed Research International،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1138011
Modern Language Association (MLA)
Abduallah, Yasser…[et al.]. MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach. BioMed Research International No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1138011
American Medical Association (AMA)
Abduallah, Yasser& Turki, Turki& Byron, Kevin& Du, Zongxuan& Cervantes-Cervantes, Miguel& Wang, Jason T. L.. MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1138011
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138011