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
Response Classification Based on a Minimal Model of Glioblastoma Growth Is Prognostic for Clinical Outcomes and Distinguishes Progression from Pseudoprogression
Maxwell Lewis Neal,Andrew D. Trister,Sunyoung Ahn,Anne Baldock,Carly Bridge,Laura Guyman,Jordan Lange,Rita Sodt,Tyler Cloke,Albert Lai,Timothy F. Cloughesy,Maciej M. Mrugala,Jason K. Rockhill,Russell C. Rockne,Kristin R. Swanson +14 more
TL;DR: A model-based metric of therapy response called Days Gained is developed that shows scores from a simple glioblastoma growth model computed at the time of the first postradiotherapy MRI scan are prognostic for time to tumor recurrence and overall patient survival.
Journal ArticleDOI
IL-6-driven STAT signalling in circulating CD4+ lymphocytes is a marker for early anticitrullinated peptide antibody-negative rheumatoid arthritis.
Amy E. Anderson,Arthur G. Pratt,Mamdouh A.K. Sedhom,John Paul Doran,Christine Routledge,Ben Hargreaves,Philip M. Brown,Kim-Anh Lê Cao,John D. Isaacs,Ranjeny Thomas +9 more
TL;DR: Findings support a particular role for IL-6-driven CD4+ T cell activation via STAT3 during the induction of RA, particularly as a feature of ACPA-negative disease.
Journal ArticleDOI
Optimal intensive care outcome prediction over time using machine learning.
Christopher Meiring,Abhishek Dixit,Steve Harris,Niall S. MacCallum,David Brealey,Peter J. Watkinson,Andrew Jones,Simon Ashworth,Richard Beale,Stephen J. Brett,Mervyn Singer,Ari Ercole +11 more
TL;DR: The assessment of prognosis over more than one day may be a valuable strategy as new information on the second day helps to differentiate outcomes, and new ML models based on trend data beyond the first day could greatly improve upon current risk stratification tools.
Journal ArticleDOI
Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection
Nan Liu,Zhi Xiong Koh,Junyang Goh,Zhiping Lin,Benjamin Haaland,Boon Ping Ting,Marcus Eng Hock Ong +6 more
TL;DR: It is observed that a few predictors outperformed the whole set of variables in predicting MACE within 72 h and machine learning-based variable selection seems promising in discovering a few relevant and significant measures as predictors.
Journal ArticleDOI
Diagnostic accuracy of content-based dermatoscopic image retrieval with deep classification features.
TL;DR: In this article, the authors compared the diagnostic accuracy obtained by using content-based image retrieval (CBIR) to retrieve visually similar dermatoscopic images with corresponding disease labels against predictions made by a neural network.
References
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