Parallel Algorithms for Inferring Gene Regulatory Networks: A Review
| Autor: | Alireza Khanteymoori, Ali Azarpeyvand, Omid Abbaszadeh |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Theoretical computer science Computer science Parallel algorithms Systems biology 0206 medical engineering Gene regulatory network Parallel algorithm Inference CUDA 02 engineering and technology Field (computer science) Article 03 medical and health sciences Genetics Parallel processing Reverse engineering Massively parallel Genetics (clinical) OpenMP 030104 developmental biology Parallel processing (DSP implementation) Hadoop MPI ComputingMethodologies_GENERAL 020602 bioinformatics |
| Zdroj: | Current Genomics |
| ISSN: | 1875-5488 1389-2029 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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