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SDRS—an algorithm for analyzing large-scale dose–response data

Summary: Dose–response information is critical to understanding drug effects, yet analytical methods for dose–response assays cannot cope with the dimensionality of large-scale screening data such as the microarray profiling data. To overcome this limitation, we developed and implemented the Sigmoid...

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Autores principales: Ji, Rui-Ru, Siemers, Nathan O., Lei, Ming, Schweizer, Liang, Bruccoleri, Robert E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3187656/
https://www.ncbi.nlm.nih.gov/pubmed/21865301
http://dx.doi.org/10.1093/bioinformatics/btr489
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author Ji, Rui-Ru
Siemers, Nathan O.
Lei, Ming
Schweizer, Liang
Bruccoleri, Robert E.
author_facet Ji, Rui-Ru
Siemers, Nathan O.
Lei, Ming
Schweizer, Liang
Bruccoleri, Robert E.
author_sort Ji, Rui-Ru
collection PubMed
description Summary: Dose–response information is critical to understanding drug effects, yet analytical methods for dose–response assays cannot cope with the dimensionality of large-scale screening data such as the microarray profiling data. To overcome this limitation, we developed and implemented the Sigmoidal Dose Response Search (SDRS) algorithm, a grid search-based method designed to handle large-scale dose–response data. This method not only calculates the pharmacological parameters for every assay, but also provides built-in statistic that enables downstream systematic analyses, such as characterizing dose response at the transcriptome level. Availability: Bio::SDRS is freely available from CPAN (www.cpan.org). Contacts: ruiruji@gmail.com; bruc@acm.org Supplementary Information: Supplementary data is available at Bioinformatics online.
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spelling pubmed-31876562011-10-05 SDRS—an algorithm for analyzing large-scale dose–response data Ji, Rui-Ru Siemers, Nathan O. Lei, Ming Schweizer, Liang Bruccoleri, Robert E. Bioinformatics Applications Note Summary: Dose–response information is critical to understanding drug effects, yet analytical methods for dose–response assays cannot cope with the dimensionality of large-scale screening data such as the microarray profiling data. To overcome this limitation, we developed and implemented the Sigmoidal Dose Response Search (SDRS) algorithm, a grid search-based method designed to handle large-scale dose–response data. This method not only calculates the pharmacological parameters for every assay, but also provides built-in statistic that enables downstream systematic analyses, such as characterizing dose response at the transcriptome level. Availability: Bio::SDRS is freely available from CPAN (www.cpan.org). Contacts: ruiruji@gmail.com; bruc@acm.org Supplementary Information: Supplementary data is available at Bioinformatics online. Oxford University Press 2011-10-15 2011-08-24 /pmc/articles/PMC3187656/ /pubmed/21865301 http://dx.doi.org/10.1093/bioinformatics/btr489 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.5 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Ji, Rui-Ru
Siemers, Nathan O.
Lei, Ming
Schweizer, Liang
Bruccoleri, Robert E.
SDRS—an algorithm for analyzing large-scale dose–response data
title SDRS—an algorithm for analyzing large-scale dose–response data
title_full SDRS—an algorithm for analyzing large-scale dose–response data
title_fullStr SDRS—an algorithm for analyzing large-scale dose–response data
title_full_unstemmed SDRS—an algorithm for analyzing large-scale dose–response data
title_short SDRS—an algorithm for analyzing large-scale dose–response data
title_sort sdrs—an algorithm for analyzing large-scale dose–response data
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3187656/
https://www.ncbi.nlm.nih.gov/pubmed/21865301
http://dx.doi.org/10.1093/bioinformatics/btr489
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