Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2008-6-24
pubmed:abstractText
As in many different areas of science and technology, most important problems in bioinformatics rely on the proper development and assessment of binary classifiers. A generalized assessment of the performance of binary classifiers is typically carried out through the analysis of their receiver operating characteristic (ROC) curves. The area under the ROC curve (AUC) constitutes a popular indicator of the performance of a binary classifier. However, the assessment of the statistical significance of the difference between any two classifiers based on this measure is not a straightforward task, since not many freely available tools exist. Most existing software is either not free, difficult to use or not easy to automate when a comparative assessment of the performance of many binary classifiers is intended. This constitutes the typical scenario for the optimization of parameters when developing new classifiers and also for their performance validation through the comparison to previous art.
pubmed:commentsCorrections
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pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1471-2105
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
9
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
265
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
StAR: a simple tool for the statistical comparison of ROC curves.
pubmed:affiliation
Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Alameda 340, Santiago, Chile. ivergara@gmail.com
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't