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Parallel mutual information estimation for inferring gene regulatory networks on GPUs

BACKGROUND: 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...

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Autores principales: Shi, Haixiang, Schmidt, Bertil, Liu, Weiguo, Müller-Wittig, Wolfgang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3138459/
https://www.ncbi.nlm.nih.gov/pubmed/21672264
http://dx.doi.org/10.1186/1756-0500-4-189
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author Shi, Haixiang
Schmidt, Bertil
Liu, Weiguo
Müller-Wittig, Wolfgang
author_facet Shi, Haixiang
Schmidt, Bertil
Liu, Weiguo
Müller-Wittig, Wolfgang
author_sort Shi, Haixiang
collection PubMed
description BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.
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spelling pubmed-31384592011-07-19 Parallel mutual information estimation for inferring gene regulatory networks on GPUs Shi, Haixiang Schmidt, Bertil Liu, Weiguo Müller-Wittig, Wolfgang BMC Res Notes Technical Note BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. BioMed Central 2011-06-15 /pmc/articles/PMC3138459/ /pubmed/21672264 http://dx.doi.org/10.1186/1756-0500-4-189 Text en Copyright ©2011 Shi et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technical Note
Shi, Haixiang
Schmidt, Bertil
Liu, Weiguo
Müller-Wittig, Wolfgang
Parallel mutual information estimation for inferring gene regulatory networks on GPUs
title Parallel mutual information estimation for inferring gene regulatory networks on GPUs
title_full Parallel mutual information estimation for inferring gene regulatory networks on GPUs
title_fullStr Parallel mutual information estimation for inferring gene regulatory networks on GPUs
title_full_unstemmed Parallel mutual information estimation for inferring gene regulatory networks on GPUs
title_short Parallel mutual information estimation for inferring gene regulatory networks on GPUs
title_sort parallel mutual information estimation for inferring gene regulatory networks on gpus
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3138459/
https://www.ncbi.nlm.nih.gov/pubmed/21672264
http://dx.doi.org/10.1186/1756-0500-4-189
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