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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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Detalles Bibliográficos
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
Descripción
Sumario: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.