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

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 un...

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Bibliographic Details
Published in:BioMed research international Vol. 2017; no. 2017; pp. 1 - 8
Main Authors: Cervantes-Cervantes, Miguel, Du, Zongxuan, Byron, Kevin, Turki, Turki, Abduallah, Yasser, Wang, Jason T. L.
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2017
Hindawi
John Wiley & Sons, Inc
Subjects:
Algorithms
Analysis
Bioinformatics
Biotechnology
Cell cycle
Cloud computing
Computer science
Datasets
Deoxyribonucleic acid
DNA
Epigenetics
Gene expression
Gene Regulatory Networks
Genetic regulation
Genomes
Genomics
Information Theory
Metabolism
Oligonucleotide Array Sequence Analysis - methods
Researchers
RNA sequencing
Saccharomyces cerevisiae - genetics
Signal transduction
Studies
Time Factors
Transcription factors
ISSN:2314-6133, 2314-6141, 2314-6141
Online Access:Get full text
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Summary: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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Academic Editor: Farit M. Afendi
ISSN:2314-6133
2314-6141
2314-6141
DOI:10.1155/2017/6261802