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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Genomic copy number predicts esophageal cancer years before transformation.
Sarah Killcoyne,Sarah Killcoyne,Eleanor Gregson,David C. Wedge,David C. Wedge,David C. Wedge,Dan J. Woodcock,Matthew D. Eldridge,Rachel de la Rue,Ahmad Miremadi,Sujath Abbas,Adrienn Blasko,Cassandra Kosmidou,Wladyslaw Januszewicz,Aikaterini Varanou Jenkins,Moritz Gerstung,Rebecca C. Fitzgerald +16 more
TL;DR: Shallow whole-genome sequencing of 777 biopsies shows that genomic signals can distinguish progressive from stable disease even 10 years before histopathological transformation, enabling earlier treatment for high-risk patients as well as reduction of unnecessary treatment and monitoring for patients who are unlikely to develop cancer.
Journal ArticleDOI
Identification of a circulating miRNA signature in extracellular vesicles collected from amyotrophic lateral sclerosis patients.
Daniel Saucier,Gabriel Wajnberg,Jeremy Roy,Annie-Pier Beauregard,Simi Chacko,Nicolas Crapoulet,Stéphanie Fournier,Anirban Ghosh,Stephen M. Lewis,Alier Marrero,Colleen O’Connell,Rodney J. Ouellette,Pier Jr Morin +12 more
TL;DR: These data identify an ALS-associated miRNAs signature in EVs of PALS and further strengthen the potential diagnostic relevance of these small molecules for this condition.
Journal ArticleDOI
Defining the "Frequent Exacerbator" Phenotype in COPD: A Hypothesis-Free Approach.
Olivier Le Rouzic,Nicolas Roche,Alexis B. Cortot,Isabelle Tillie-Leblond,F. Masure,Thierry Perez,Isabelle Boucot,Latifa Hamouti,Juliette Ostinelli,Céline Pribil,Christine Poutchnine,Stéphane Schück,Mathilde Pouriel,Bruno Housset +13 more
TL;DR: Analysis of prospectively recorded exacerbations confirmed the existence and clinical relevance of a frequent exacerbator subgroup of patients with COPD and the currently used threshold to define this phenotype.
Journal ArticleDOI
Validation of a Genome-Wide Polygenic Score for Coronary Artery Disease in South Asians
Minxian Wang,Ramesh Menon,Sanghamitra Mishra,Aniruddh P. Patel,Mark Chaffin,Deepak Tanneeru,Manjari Deshmukh,Oshin Mathew,Sanika Apte,Christina S. Devanboo,Sumathi Sundaram,Praveena Lakshmipathy,Sakthivel Murugan,Krishna Kumar Sharma,Karthikeyan Rajendran,Sam Santhosh,Rajesh Thachathodiyl,Hisham Ahamed,Aniketh Vijay Balegadde,Thomas Alexander,Krishnan Swaminathan,Rajeev Gupta,Ajit S. Mullasari,Alben Sigamani,Muralidhar Kanchi,Andrew S. Peterson,Adam S. Butterworth,John Danesh,Emanuele Di Angelantonio,Aliya Naheed,Michael Inouye,Rajiv Chowdhury,Ramprasad Vedam,Sekar Kathiresan,Ravi Gupta,Amit Khera +35 more
TL;DR: The new GPSCAD has been developed and tested using 3 distinct South Asian studies, and provides a generalizable framework for ancestry-specific GPS assessment.
Journal ArticleDOI
Toward robust mammography-based models for breast cancer risk.
Adam Yala,Peter G. Mikhael,Fredrik Strand,Fredrik Strand,Gigin Lin,Kevin Smith,Kevin Smith,Yung-Liang Wan,Leslie R Lamb,Kevin S. Hughes,Constance D. Lehman,Regina Barzilay +11 more
TL;DR: In this article, a mammography-based deep learning model is proposed to predict risk at multiple timepoints, leverage potentially missing risk factor information, and produce predictions that are consistent across mammography machines.
References
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