Document Detail


A Software Framework for Building Biomedical Machine Learning Classifiers through Grid Computing Resources.
MedLine Citation:
PMID:  21479625     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
This paper describes the BiomedTK software framework, created to perform massive explorations of machine learning classifiers configurations for biomedical data analysis over distributed Grid computing resources. BiomedTK integrates ROC analysis throughout the complete classifier construction process and enables explorations of large parameter sweeps for training third party classifiers such as artificial neural networks and support vector machines, offering the capability to harness the vast amount of computing power serviced by Grid infrastructures. In addition, it includes classifiers modified by the authors for ROC optimization and functionality to build ensemble classifiers and manipulate datasets (import/export, extract and transform data, etc.). BiomedTK was experimentally validated by training thousands of classifier configurations for representative biomedical UCI datasets reaching in little time classification levels comparable to those reported in existing literature. The comprehensive method herewith presented represents an improvement to biomedical data analysis in both methodology and potential reach of machine learning based experimentation.
Authors:
Raúl Ramos-Pollán; Miguel Angel Guevara-López; Eugénio Oliveira
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-4-9
Journal Detail:
Title:  Journal of medical systems     Volume:  -     ISSN:  0148-5598     ISO Abbreviation:  -     Publication Date:  2011 Apr 
Date Detail:
Created Date:  2011-4-11     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7806056     Medline TA:  J Med Syst     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
CETA-CIEMAT Centro Extremeño de Tecnologías Avanzadas, Calle Sola 1, 10200, Trujillo, Spain, raul.ramos@ciemat.es.
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