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.

中文翻译：

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推断基因调控网络的并行算法：综述


摘要：系统生物学问题，例如从大规模基因表达数据构建全基因组网络，是复杂且耗时的。因此，使用顺序算法无法在可接受的时间内获得解决方案。如今，通过使用大规模并行计算，可以推断出大规模的基因调控网络。近年来，从大规模数据集建立基因调控网络引起了并行计算和系统生物学领域研究人员的广泛关注。在本文中，我们试图对最近构建基因调控网络的并行算法进行更详细的概述。首先，给出了基因调控网络推理的基础知识和大规模数据集的挑战。其次，详细介绍了 CUDA、OpenMP、MPI 和 Hadoop 四种并行框架和库。第三，回顾了并行算法。最后，描述了并行逆向工程的一些结论和指南。