Parallel mutual information estimation for inferring gene regulatory networks on GPUs.
Parallel mutual information estimation for inferring gene regulatory networks on GPUs.
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DOI：
10.1186/1756-0500-4-189
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发表时间：
2011-06-15
影响因子：
1.8
通讯作者：
Müller-Wittig W
中科院分区：
文献类型：
--
作者：
Shi H;Schmidt B;Liu W;Müller-Wittig W
互信息是衡量两个变量之间相似性的指标。它在计算生物学、机器学习、统计学、图像处理、金融计算等领域有着广泛的应用。以前使用的简单的基于直方图的互信息估计器与基于核的方法相比在质量上缺乏精度。最近提出的基于B-Spline函数的互信息估计方法在质量上与基于核的方法相当，但计算复杂度较低。提出了一种利用商用图形硬件加速B-Spline函数互信息估计算法的新方法。为了有效地映射到这种类型的体系结构，我们使用了计算统一设备体系结构(CUDA)编程模型来设计和实现一种新的并行算法。我们的实现，称为CUDA-MI，与在四核CPU上针对大型微阵列数据集的多线程实现相比，在单GPU上使用双精度可以实现高达82的加速比。我们使用CUDA-MI获得的结果从微阵列数据中推断基因调控网络(GRN)。与ARACNE和TING等现有方法的比较表明，CUDA-MI在更短的时间内生成了更高质量的GRN。CUDA-MI是公开可用的开源软件，用CUDA和C++编程语言编写。它通过充分利用常用的支持CUDA的低成本GPU的计算能力，实现了与顺序多线程实施相比的显著加速。
Mutual information is a measure of similarity between two variables. It has been widely used in various application domains including computational biology, machine learning, statistics, image processing, and financial computing. Previously used simple histogram based mutual information estimators lack the precision in quality compared to kernel based methods. The recently introduced B-spline function based mutual information estimation method is competitive to the kernel based methods in terms of quality but at a lower computational complexity. We present a new approach to accelerate the B-spline function based mutual information estimation algorithm with commodity graphics hardware. To derive an efficient mapping onto this type of architecture, we have used the Compute Unified Device Architecture (CUDA) programming model to design and implement a new parallel algorithm. Our implementation, called CUDA-MI, can achieve speedups of up to 82 using double precision on a single GPU compared to a multi-threaded implementation on a quad-core CPU for large microarray datasets. We have used the results obtained by CUDA-MI to infer gene regulatory networks (GRNs) from microarray data. The comparisons to existing methods including ARACNE and TINGe show that CUDA-MI produces GRNs of higher quality in less time. CUDA-MI is publicly available open-source software, written in CUDA and C++ programming languages. It obtains significant speedup over sequential multi-threaded implementation by fully exploiting the compute capability of commonly used CUDA-enabled low-cost GPUs.