Parallel Algorithms for Inferring Gene Regulatory Networks: A Review

Title:Parallel Algorithms for Inferring Gene Regulatory Networks: A Review

Volume: 19 Issue: 7

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

Affiliation:

• Department of Electrical and Computer Engineering, University of Zanjan, Zanjan,Iran

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

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.

Cite this article as:

Abbaszadeh Omid, Khanteymoori Reza Ali *, Azarpeyvand Ali, Parallel Algorithms for Inferring Gene Regulatory Networks: A Review, Current Genomics 2018; 19 (7) . https://dx.doi.org/10.2174/1389202919666180601081718