{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["12"],"submitter":["Robin X"],"pubmed_abstract":["<h4>Background</h4>Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface.<h4>Results</h4>With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods fo"],"journal":["BMC bioinformatics"],"pagination":["77"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC3068975"],"repository":["biostudies-literature"],"pubmed_title":["pROC: an open-source package for R and S+ to analyze and compare ROC curves."],"pmcid":["PMC3068975"],"pubmed_authors":["Turck N","Muller M","Tiberti N","Lisacek F","Hainard A","Sanchez JC","Robin X"],"additional_accession":[]},"is_claimable":false,"name":"pROC: an open-source package for R and S+ to analyze and compare ROC curves.","description":"<h4>Background</h4>Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface.<h4>Results</h4>With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods fo","dates":{"release":"2011-01-01T00:00:00Z","publication":"2011 Mar","modification":"2025-04-21T21:14:10.228Z","creation":"2019-03-27T00:40:17Z"},"accession":"S-EPMC3068975","cross_references":{"pubmed":["21414208"],"doi":["10.1186/1471-2105-12-77"]}}