Parallel mutual information estimation for inferring gene regulatory networks on GPUs

Publication type:
Journal article
Metadata:
Autoren
DOI
10.1186/1756-0500-4-189
eISSN
1756-0500
Ausgabe der Veröffentlichung
1
Zeitschrift
BMC Research Notes
Sprache
en
Artikelnummer
189
Online publication date
2011
Datum der Veröffentlichung
2011
Status
Published
Herausgeber
Springer Science and Business Media LLC
Herausgeber URL
http://dx.doi.org/10.1186/1756-0500-4-189
Datum der Datenerfassung
2019
Titel
Parallel mutual information estimation for inferring gene regulatory networks on GPUs
Ausgabe der Zeitschrift
4

Data source: Crossref

Other metadata sources:
Abstract
<h4>Background</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.
Addresses
  • School of Computer Engineering, Nanyang Technological University, Singapore. hxshi@ntu.edu.sg.
Autoren
DOI
10.1186/1756-0500-4-189
eISSN
1756-0500
Externe Identifier
  • PubMed Identifier: 21672264
  • PubMed Central ID: PMC3138459
Open access
true
ISSN
1756-0500
Zeitschrift
BMC research notes
Sprache
eng
Medium
Electronic
Online publication date
2011
Open access status
Open Access
Paginierung
189
Datum der Veröffentlichung
2011
Status
Published
Publisher licence
CC BY
Datum der Datenerfassung
2011
Titel
Parallel mutual information estimation for inferring gene regulatory networks on GPUs.
Sub types
  • research-article
  • Journal Article
Ausgabe der Zeitschrift
4

Files

https://bmcresnotes.biomedcentral.com/track/pdf/10.1186/1756-0500-4-189 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21672264/pdf/?tool=EBI https://europepmc.org/articles/PMC3138459?pdf=render

Data source: Europe PubMed Central

Abstract
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.
Date of acceptance
2011
Autoren
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/21672264
DOI
10.1186/1756-0500-4-189
eISSN
1756-0500
Externe Identifier
  • PubMed Central ID: PMC3138459
Zeitschrift
BMC Res Notes
Sprache
eng
Country
England
Paginierung
189
PII
1756-0500-4-189
Datum der Veröffentlichung
2011
Status
Published online
Datum, an dem der Datensatz öffentlich gemacht wurde
2011
Titel
Parallel mutual information estimation for inferring gene regulatory networks on GPUs.
Sub types
  • Journal Article
Ausgabe der Zeitschrift
4

Data source: PubMed

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