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
The Effects of Obstructive Sleep Apnea Syndrome on the Dentate Gyrus and Learning and Memory in Children
Jiook Cha,Johanna A. Zea-Hernandez,Sanghun Sin,Katharina D. Graw-Panzer,Keivan Shifteh,Carmen R. Isasi,Mark E. Wagshul,Eileen E. Moran,Jonathan Posner,Molly E. Zimmerman,Raanan Arens +10 more
TL;DR: Lower mean diffusivity of the dentate gyrus in children with OSAS is demonstrated, which correlates with a lower verbal learning and memory score, which may help explain some of the neurocognitive deficits described in these children.
Journal ArticleDOI
Integrative metagenomic and metabolomic analyses reveal severity-specific signatures of gut microbiota in chronic kidney disease
I-Wen Wu,I-Wen Wu,Sheng-Siang Gao,Hsin-Cheng Chou,Huang-Yu Yang,Huang-Yu Yang,Huang-Yu Yang,Lun-Ching Chang,Yu-Lun Kuo,Michael Cong Vinh Dinh,Wen-Hung Chung,Chi-Wei Yang,Chi-Wei Yang,Hsin-Chih Lai,Hsin-Chih Lai,Wen-Ping Hsieh,Shih-Chi Su +16 more
TL;DR: Dual-omics data reveal the connections between intestinal microbes and circulating metabolites perturbed in CKD, which may be of etiological and diagnostic importance.
Journal ArticleDOI
Diffusion Tensor Imaging Adds Diagnostic Accuracy in Magnetic Resonance Neurography.
Michael O. Breckwoldt,Christian Stock,Annie Xia,Andreas Heckel,Martin Bendszus,Mirko Pham,Sabine Heiland,Philipp Bäumer +7 more
TL;DR: Combining DTI with T2 can outperform T2-w imaging alone and provides added value in magnetic resonance neurography and the combination of nT2 with DTI parameters yielded excellent adjusted AUCs up to 0.97 (nT2 + FA).
Journal ArticleDOI
Performance of the MasSpec Pen for Rapid Diagnosis of Ovarian Cancer
Marta Sans,Jialing Zhang,John Q. Lin,Clara L. Feider,Noah Giese,Michael T. Breen,Katherine R. Sebastian,Jinsong Liu,Anil K. Sood,Livia S. Eberlin +9 more
TL;DR: The MasSpec Pen, together with machine learning, provides robust molecular models for ovarian serous cancer prediction and thus has potential for clinical use for rapid and accurate ovarian cancer diagnosis.
Journal ArticleDOI
Non-HDL-cholesterol/HDL-cholesterol is a better predictor of metabolic syndrome and insulin resistance than apolipoprotein B/apolipoprotein A1
Se Won Kim,Jae Hwan Jee,Hye Jeong Kim,Sang-Man Jin,Sunghwan Suh,Ji Cheol Bae,Sun Wook Kim,Jae Hoon Chung,Yong Ki Min,Myung-Shik Lee,Moon-Kyu Lee,Kwang-Won Kim,Jae Hyeon Kim +12 more
TL;DR: The findings indicate that the non-HDL-C/HDL -C ratio is a better marker than the apoB/apoA1 ratio for identifying IR and MetS in Koreans.
References
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