Results 211 to 220 of about 364,272 (265)

Ensuring reliable cave temperature data for climate change research. [PDF]

Sci Rep
Cimenti A, Cresi L, Isaia M, Piano E, Piquet A, Nicolosi G, Mammola S, Senese A, Acquaotta F.   +8 more
europepmc   +1 more source

Expanding and Enhancing Neural Network-Based QM-AI Models for the Accurate Prediction of Halogen-π Interaction Energies in Protein Contexts. [PDF]

J Chem Inf Model
Engelhardt MU, Mier F, Zimmermann MO, Boeckler FM.   +3 more
europepmc   +1 more source

A multidisciplinary RNA-guided approach to complement genomic analysis of unsolved patients with an inborn error of immunity. [PDF]

Front Immunol
Maassen WTK, Pape LCET, Niemeijer T, Heijink AM, Meems-Veldhuis MT, Boerrigter DJ, van der Vries G, Westers H, Johansson LF, van der Velde KJ, Swertz MA, Franke L, Legger GE, Lambeck AJA, Rutgers A, Jonkers IH, van Gijn ME, Zonneveld-Huijssoon E.   +17 more
europepmc   +1 more source

Cut point validation vs. real-world performance: the ultra-low cut point dilemma. [PDF]

Bioanalysis
Denhoff L, Dernek C, Heins L, Gu C, Renfro A, Pasas-Farmer S, Kernstock R.   +6 more
europepmc   +1 more source

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Adaptive outlierness for subspace outlier ranking

Proceedings of the 19th ACM international conference on Information and knowledge management, 2010
Outlier mining is an important data analysis task to distinguish exceptional outliers from regular objects. However, in recent applications traditional outlier mining approaches miss outliers as they are hidden in subspace projections. In this work, we propose a novel outlier ranking based on the degree of deviation in subspaces.
Emmanuel Müller, Matthias Schiffer, Thomas Seidl 0001   +2 more
openaire   +1 more source

On Evaluation of Outlier Rankings and Outlier Scores

Proceedings of the 2012 SIAM International Conference on Data Mining, 2012
Outlier detection research is currently focusing on the development of new methods and on improving the computation time for these methods. Evaluation however is rather heuristic, often considering just precision in the top k results or using the area under the ROC curve.
Erich Schubert, Remigius Wojdanowski, Arthur Zimek, Hans-Peter Kriegel   +3 more
openaire   +1 more source

The Outlier

Hastings Center Report, 2013
AbstractAmy's excellent home care was the only reason she was doing as well as she was. There was no one better at taking care of Amy than her husband. When she had a respiratory infection, Steve managed the necessary increased suctioning, nebulizer treatments, and ventilator, in addition to all the other intimate personal assistance she needed on a ...
openaire   +2 more sources

When is an outlier not an outlier?

NIR news, 2019
The tendency of the users of NIRS is to think of the composition particularly in terms of the reference data, for the prediction of which the technique is to be used. The concept and principle of NIRS analysis differ from classical chemical analysis in that NIRS is based exclusively on the spectra.
openaire   +1 more source

Outlier detection

WIREs Data Mining and Knowledge Discovery, 2011
AbstractOutlier detection is an area of research with a long history which has applications in many fields. This article provides a nontechnical and concise overview of the commonly used approaches for detecting outliers, including classical methods, new challenges posed by real‐world massive data, and some of the key advances made in recent years ...
Xiaogang Su, Chih-Ling Tsai
openaire   +1 more source