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
Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019
Lu Shan Xiao,Pu Li,Fenglong Sun,Yan-Pei Zhang,Chenghai Xu,Hongbo Zhu,Hongbo Zhu,Feng Qin Cai,Yu Lin He,Wenfeng Zhang,Si Cong Ma,Chenyi Hu,Mengchun Gong,Li Liu,Wenzhao Shi,Hong Zhu +15 more
TL;DR: A deep learning-based model can accurately predict disease severity as well as disease progression in COVID-19 patients using CT imaging, offering promise for guiding clinical treatment.
Journal ArticleDOI
The value of noninvasive measurement of the compensatory reserve index in monitoring and triage of patients experiencing minimal blood loss.
Roy Nadler,Victor A. Convertino,Sami Gendler,Gadi Lending,Ari M. Lipsky,Sylvain Cardin,Alexander Lowenthal,Elon Glassberg +7 more
TL;DR: The Compensatory Reserve Index was better than standard indices in detecting mild blood loss and may enable a more accurate triage and CRI monitoring may allow for earlier detection of casualty deterioration.
Journal ArticleDOI
Effects of maternal investment, temperament, and cognition on guide dog success.
Emily E. Bray,Emily E. Bray,Mary D. Sammel,Dorothy L. Cheney,James A. Serpell,Robert M. Seyfarth +5 more
TL;DR: It is found that high levels of overall maternal behavior were linked with a higher likelihood of program failure, and both maternal nursing behavior and individual traits of cognition and temperament are associated with guide dog success.
Journal ArticleDOI
Leptin, adiponectin, and their ratio as markers of insulin resistance and cardiometabolic risk in childhood obesity
Christine Frithioff-Bøjsøe,Christine Frithioff-Bøjsøe,Morten Asp Vonsild Lund,Morten Asp Vonsild Lund,Ulrik Lausten-Thomsen,Paula L. Hedley,Oluf Pedersen,Michael Christiansen,Michael Christiansen,Jennifer L. Baker,Jennifer L. Baker,Torben Hansen,Torben Hansen,Jens-Christian Holm,Jens-Christian Holm +14 more
TL;DR: It is imperative to develop markers for risk stratification and detection of cardiometabolic comorbidities in children with obesity.
Journal ArticleDOI
Serum metabolomics reveals higher levels of polyunsaturated fatty acids in lepromatous leprosy: potential markers for susceptibility and pathogenesis.
Reem Al-Mubarak,Jason A. Vander Heiden,Corey D. Broeckling,Marivic Balagon,Patrick J. Brennan,Varalakshmi D. Vissa +5 more
TL;DR: A metabolomics approach using high mass accuracy ultrahigh pressure liquid chromatography mass spectrometry to investigate the circulatory biomarkers in newly diagnosed untreated leprosy patients found significant increases in the abundance of certain polyunsaturated fatty acids (PUFAs) and phospholipids in the high-BI patients, when contrasted with the levels in the low-BI customers.
References
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