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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Use of a machine learning framework to predict substance use disorder treatment success.
Laura Acion,Laura Acion,Diana Kelmansky,Mark J. van der Laan,Ethan Sahker,DeShauna Jones,Stephan Arndt,Stephan Arndt +7 more
TL;DR: This work compares the performance of logistic regression, penalized regression, random forests, deep learning neural networks, and SL to predict successful substance use disorders (SUD) treatment and concludes that SL was superior to all but one of the algorithms compared.
Journal ArticleDOI
Tongue coating microbiome data distinguish patients with pancreatic head cancer from healthy controls
Haifeng Lu,Zhigang Ren,Ang Li,Ang Li,Jinyou Li,Shao-Yan Xu,Hua Zhang,Jianwen Jiang,Jiezuan Yang,Qixia Luo,Kai Zhou,Shusen Zheng,Lanjuan Li +12 more
TL;DR: The microbiota dysbiosis of the tongue coat in PHC patients is identified, and insight is provided into the association between the human microbiome and pancreatic cancer.
Journal ArticleDOI
Label Co-Occurrence Learning With Graph Convolutional Networks for Multi-Label Chest X-Ray Image Classification
TL;DR: This paper proposes a novel label co-occurrence learning framework based on Graph Convolution Networks (GCNs) to explicitly explore the dependencies between pathologies for the multi-label chest X-ray (CXR) image classification task, which is term the “CheXGCN”.
Journal ArticleDOI
Prediction of human drug-induced liver injury (DILI) in relation to oral doses and blood concentrations
Wiebke Albrecht,Franziska Kappenberg,Tim Brecklinghaus,Regina Stoeber,Rosemarie Marchan,Mian Zhang,Kristina E Ebbert,Hendrik Kirschner,Marianna Grinberg,Marianna Grinberg,Marcel Leist,Wolfgang Moritz,Cristina Cadenas,Ahmed Ghallab,Ahmed Ghallab,Jörg Reinders,Nachiket Vartak,Christoph van Thriel,Klaus Golka,Laia Tolosa,José V. Castell,Georg Damm,Georg Damm,Daniel Seehofer,Daniel Seehofer,Alfonso Lampen,Albert Braeuning,Thorsten Buhrke,Anne Cathrin Behr,Axel Oberemm,Xiaolong Gu,Naim Kittana,Bob van de Water,Reinhard Kreiling,Susann Fayyaz,Leon van Aerts,Bård Smedsrød,Heidrun Ellinger-Ziegelbauer,Thomas Steger-Hartmann,Ursula Gundert-Remy,Anja Zeigerer,Anett Ullrich,Dieter Runge,Serene M. L. Lee,Tobias S. Schiergens,Lars Kuepfer,Alejandro Aguayo-Orozco,Agapios Sachinidis,Karolina Edlund,Iain Gardner,Jörg Rahnenführer,Jan G. Hengstler +51 more
TL;DR: An in vitro/in silico method was established that predicts the risk of human DILI in relation to oral doses and blood concentrations of test compounds to the probability of hepatotoxicity and application to the rat hepatotoxicant pulegone resulted in an ADI similar to values previously established based on animal experiments.
Journal ArticleDOI
Validation of a Host Response Assay, SeptiCyte LAB, for Discriminating Sepsis from Systemic Inflammatory Response Syndrome in the ICU.
Russell R. Miller,Russell R. Miller,Bert K. Lopansri,Bert K. Lopansri,John P. Burke,John P. Burke,Mitchell M. Levy,Steven M. Opal,Richard E. Rothman,Franco R. D'Alessio,Venkataramana K. Sidhaye,Neil R. Aggarwal,Robert A. Balk,Jared A. Greenberg,Mark Yoder,Gourang P. Patel,Emily R. Gilbert,Majid Afshar,Jorge P. Parada,Greg S. Martin,Annette M. Esper,Jordan A. Kempker,Mangala Narasimhan,Adey Tsegaye,Stella Hahn,Paul H. Mayo,Tom van der Poll,Marcus J. Schultz,Brendon P. Scicluna,Peter M. C. Klein Klouwenberg,Antony Rapisarda,Therese Seldon,Leo McHugh,Thomas D. Yager,Silvia Cermelli,Dayle Sampson,Victoria Rothwell,Richard Newman,Shruti Bhide,Brian A. Fox,James T. Kirk,Krupa Arun Navalkar,Roy F. Davis,Roslyn A. Brandon,Richard Bruce Brandon +44 more
TL;DR: SeptiCyte LAB appears to be a promising diagnostic tool to complement physician assessment of infection likelihood in critically ill adult patients with systemic inflammation.
References
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