摘要
为解决大规模基因调控网络构建算法精度不高、计算时间过长的问题,提出一种从基因表达数据分析出发,并行计算和阈值限定相结合的新算法来构建大规模基因调控网络。该算法中基因间交互强度值采用条件互信息值度量,并行计算采用GPU与CPU相结合的CUDA与Open MP架构。综合数据集的运行结果证明该算法较新的构建算法(如贝叶斯模型算法和微分方程模型算法)相比,在构建大规模基因调控网络时有更高的运算精度和更短的运行时间。
In order to resolve the drawbacks of traditional gene network reconstruction algorithm,such as less accuracy and long running time,this paper proposed a novel algorithm.This algorithm used parallel computational algorithm with parallel computing and threshold measurement.It measured the interaction between genes by conditional mutual information.It used the CUDA and OpenMP structure based on GPU and CPU in parallel computing algorithm.Running tests on synthetic datasets demonstrate that this algorithm can handle large datasets with more accuracy and less running test than traditional algorithm,such as Bayesian model algorithm and differential equation model algorithm.
作者
郑明
周柚
卓慕瑰
Zheng Ming;Zhou You;Zhuo Mugui(Guangxi Colleges&Universities Key Laboratory of Professional Software Technology,Wuzhou University,Wuzhou Guangxi 543000,China;Information&Electronic Engineering College,Wuzhou University,Wuzhou Guangxi 543000,China;College of Computer Science&Technology,Jilin University,Changchun 130012,China)
出处
《计算机应用研究》
CSCD
北大核心
2020年第7期2041-2044,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(61862056,61772227)
广西自然科学基金项目面上项目(2017GXNSFAA198148)
梧州学院院级项目(2017B001)
广西高校行业软件技术重点实验室项目。
关键词
基因调控网络
大规模数据集
并行计算
阈值限定
基因表达数据
gene regulatory networks
large-scale dataset
parallel computing
threshold qualification
gene expression data
