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
Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning
Charlotte Küpper,Sanna Stroth,Nicole Wolff,Florian Hauck,Natalia Kliewer,Tanja Schad-Hansjosten,Inge Kamp-Becker,Luise Poustka,Veit Roessner,Katharina Schultebraucks,Katharina Schultebraucks,Stefan Roepke +11 more
TL;DR: This work used a machine learning algorithm (support vector machine) to examine whether ASD detection can be improved by identifying a subset of behavioral features from the ADOS Module 4 in a routine clinical sample of high-functioning adolescents and adults with ASD.
Journal ArticleDOI
Performance of Doppler-based resistive index and semi-quantitative renal perfusion in predicting persistent AKI: results of a prospective multicenter study
Michael Darmon,Michael Darmon,Michael Darmon,Aurélie Bourmaud,Marie Reynaud,Stéphane Rouleau,Ferhat Meziani,Ferhat Meziani,Alexandra Boivin,Mourad Benyamina,François Vincent,Alexandre Lautrette,Christophe Leroy,Yves Cohen,Matthieu Legrand,Matthieu Legrand,Jérôme Morel,Jeremy Terreaux,David Schnell +18 more
TL;DR: Although statistically associated with AKI occurrence, RI and SQP perform poorly in predicting persistent AKI at day 3 and further studies are needed to adequately describe factors influencing Doppler-based assessment of renal perfusion.
Journal ArticleDOI
PGM5: a novel diagnostic and prognostic biomarker for liver cancer.
TL;DR: PGM5 expression was significantly lower in cancerous than adjacent normal liver tissues, and had modest diagnostic value based on ROC analysis and calculations of area under the curve (AUC), and PGM5 was independently associated with patient prognosis.
Journal ArticleDOI
Rt-based memory detection: item saliency effects in the single-probe and the multiple-probe protocol
TL;DR: In this article, the authors explored two potential moderators: item saliency and test protocol, and concluded that pronounced differences in item salience affect the validity of RT-based memory detection, and recommend the multiple-probe protocol for RT based memory detection.
Journal ArticleDOI
Prediction of mRNA subcellular localization using deep recurrent neural networks.
TL;DR: RNATracker is a novel deep neural network built to predict, from their sequence alone, the distributions of mRNA transcripts over a predefined set of subcellular compartments, and several aspects of the model can be isolated to yield valuable, testable mechanistic hypotheses, and to locate candidate zipcode sequences within transcripts.
References
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