pROC: an open-source package for R and S+ to analyze and compare ROC curves
Xavier Robin,Natacha Turck,Alexandre Hainard,Natalia Tiberti,Frédérique Lisacek,Jean-Charles Sanchez,Markus Müller +6 more
TLDR
pROC as mentioned in this paper is 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.Abstract:
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. 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 for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/
under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.read more
Citations
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Journal ArticleDOI
A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury.
Pekka Kohonen,Juuso Parkkinen,Egon Willighagen,Egon Willighagen,Rebecca Ceder,Krister Wennerberg,Samuel Kaski,Roland C. Grafström +7 more
TL;DR: A ‘big data compacting and data fusion’—concept to capture diverse adverse outcomes on cellular and organismal levels is utilized to capture unanticipated harmful effects of chemicals and drug molecules.
Journal ArticleDOI
MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression.
Aleksandra Lebedeva,Eric Westman,Tom Borza,Mona K. Beyer,Knut Engedal,Dag Aarsland,Geir Selbæk,Asta Håberg +7 more
TL;DR: LDD patients developing MCI and dementia can be discriminated from LLD patients remaining cognitively stable with good accuracy based on baseline structural MRI alone, and ventral diencephalon might play an important role in preservation of cognitive functions in LLD.
Journal ArticleDOI
Cord serum lipidome in prediction of islet autoimmunity and type 1 diabetes.
Matej Orešič,Peddinti Gopalacharyulu,Juha Mykkänen,Niina Lietzen,Marjaana Mäkinen,Heli Nygren,Satu Simell,Ville Simell,Heikki Hyöty,Riitta Veijola,Jorma Ilonen,Jorma Ilonen,Marko Sysi-Aho,Mikael Knip,Tuulia Hyötyläinen,Olli Simell +15 more
TL;DR: It is found that T1D progressors are characterized by a distinct cord blood lipidomic profile that includes reduced major choline-containing phospholipids, including sphingomyelins and phosphatidylcholines.
Journal ArticleDOI
NIH peer review percentile scores are poorly predictive of grant productivity
TL;DR: It is reported that these percentile scores awarded by peer review panels are a poor discriminator of productivity, which underscores the limitations of peer review as a means of assessing grant applications in an era when typical success rates are often as low as about 10%.
Journal ArticleDOI
Assessment of Luminal and Basal Phenotypes in Bladder Cancer.
Charles C. Guo,Jolanta Bondaruk,Hui Yao,Ziqiao Wang,Li Zhang,Sangkyou Lee,June Goo Lee,David Cogdell,Miao Zhang,Guoliang Yang,Vipulkumar Dadhania,Woonyoung Choi,Peng Wei,Jianjun Gao,Dan Theodorescu,Christopher J. Logothetis,Colin P.N. Dinney,Marek Kimmel,John N. Weinstein,David J. McConkey,Bogdan Czerniak +20 more
TL;DR: A quantitative classifier referred to as basal to luminal transition (BLT) score is developed which identified the molecular subtypes of bladder cancer with 80–94% sensitivity and 83–93% specificity.
References
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BookDOI
Modern Applied Statistics with S
W. N. Venables,Brian D. Ripley +1 more
TL;DR: A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods.
Journal ArticleDOI
An introduction to ROC analysis
TL;DR: The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.
Journal ArticleDOI
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.
TL;DR: A nonparametric approach to the analysis of areas under correlated ROC curves is presented, by using the theory on generalized U-statistics to generate an estimated covariance matrix.
Journal ArticleDOI
A method of comparing the areas under receiver operating characteristic curves derived from the same cases.
TL;DR: This paper refines the statistical comparison of the areas under two ROC curves derived from the same set of patients by taking into account the correlation between the areas that is induced by the paired nature of the data.
Modern Applied Statistics With S
TL;DR: The modern applied statistics with s is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can download it instantly.