Parallel Algorithms for Inferring Gene Regulatory Networks: A Review

Author(s): Omid Abbaszadeh, Ali Reza Khanteymoori*, Ali Azarpeyvand

Journal Name: Current Genomics

Volume 19 , Issue 7 , 2018

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Graphical Abstract:


System biology problems such as whole-genome network construction from large-scale gene expression data are sophisticated and time-consuming. Therefore, using sequential algorithms are not feasible to obtain a solution in an acceptable amount of time. Today, by using massively parallel computing, it is possible to infer large-scale gene regulatory networks. Recently, establishing gene regulatory networks from large-scale datasets have drawn the noticeable attention of researchers in the field of parallel computing and system biology. In this paper, we attempt to provide a more detailed overview of the recent parallel algorithms for constructing gene regulatory networks. Firstly, fundamentals of gene regulatory networks inference and large-scale datasets challenges are given. Secondly, a detailed description of the four parallel frameworks and libraries including CUDA, OpenMP, MPI, and Hadoop is discussed. Thirdly, parallel algorithms are reviewed. Finally, some conclusions and guidelines for parallel reverse engineering are described.

Keywords: Gene regulatory network, Parallel algorithms, Parallel processing, Reverse engineering, CUDA, OpenMP, MPI, Hadoop.

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Article Details

Year: 2018
Published on: 27 August, 2018
Page: [603 - 614]
Pages: 12
DOI: 10.2174/1389202919666180601081718
Price: $65

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