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
Identification of Candidate miRNA Biomarkers for Glaucoma
Allyson G. Hindle,Robrecht Thoonen,Jessica V Jasien,Robert M. H. Grange,Krishna Amin,Jasen Wise,Mineo Ozaki,Robert Ritch,Rajeev Malhotra,Emmanuel S. Buys +9 more
TL;DR: These results identify specific miRNAs as potential biomarkers and provide insight into the molecular processes underlying glaucoma.
Journal ArticleDOI
A proposed method to predict preterm birth using clinical data, standard maternal serum screening, and cholesterol.
Brandon W. Alleman,Amanda R. Smith,Heather M. Byers,Bruce Bedell,Kelli K. Ryckman,Jeffrey C. Murray,Kristi Borowski +6 more
TL;DR: Validation and replication in other populations, and incorporation of other measures that identify PTB risk, like cervical length, can be a step toward identifying additional women who may benefit from new or currently available interventions.
Journal ArticleDOI
Parkinson disease polygenic risk score is associated with Parkinson disease status and age at onset but not with alpha-synuclein cerebrospinal fluid levels.
Laura Ibanez,Umber Dube,Benjamin Saef,John P. Budde,Kathleen Black,Alexandra Medvedeva,Jorge L. Del-Aguila,Albert A. Davis,Joel S. Perlmutter,Oscar Harari,Bruno A. Benitez,Carlos Cruchaga +11 more
TL;DR: There is an overlap in the genetic architecture of Parkinson’s Disease risk and onset, although the different loci present different weights for those phenotypes, according to the genome-wide loci used.
Journal ArticleDOI
Identification of pathogenic missense mutations using protein stability predictors.
TL;DR: It is observed that the utility of computational stability predictors is highly heterogeneous across different proteins, and that they are all inferior to the best performing variant effect predictors for identifying pathogenic mutations.
Journal ArticleDOI
Symptoms and symptom clusters associated with SARS-CoV-2 infection in community-based populations: Results from a statewide epidemiological study.
Brian E. Dixon,Kara Wools-Kaloustian,William F. Fadel,Thomas J. Duszynski,Constantin T. Yiannoutsos,Paul K. Halverson,Nir Menachemi +6 more
TL;DR: In this article, the authors identified key symptoms and symptom combinations in a community-based population using robust methods and employed multivariable logistic regression and exploratory factor analysis (EFA) to examine symptoms and combinations associated with SARS-CoV-2 infection.
References
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