title: MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach.
abstract: 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.
authors.affiliation: Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA.
authors.forename: Yasser
authors.initials: Y
authors.lastname: Abduallah
authors.affiliation: Computer Science Department, King Abdulaziz University, P.O. Box 80221, Jeddah 21589, Saudi Arabia.
authors.forename: Turki
authors.identifier: 0000-0002-9491-2435
authors.initials: T
authors.lastname: Turki
authors.affiliation: Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA; Bioinformatics Program, New Jersey Institute of Technology, Newark, NJ 07102, USA.
authors.forename: Kevin
authors.initials: K
authors.lastname: Byron
authors.affiliation: Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA.
authors.forename: Zongxuan
authors.initials: Z
authors.lastname: Du
authors.affiliation: Department of Biological Sciences, Rutgers University, Newark, NJ 07102, USA.
authors.forename: Miguel
authors.initials: M
authors.lastname: Cervantes-Cervantes
authors.affiliation: Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA; Bioinformatics Program, New Jersey Institute of Technology, Newark, NJ 07102, USA.
authors.forename: Jason T L
authors.identifier: 0000-0002-2486-1097
authors.initials: JT
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D053263:Gene Regulatory Networks; D007257:Information Theory; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D013997:Time Factors