Results 181 to 190 of about 2,265,098 (244)

PvaPy streaming framework for real-time data processing. [PDF]

J Synchrotron Radiat
Veseli S, Hammonds J, Henke S, Parraga H, Frosik B, Schwarz N.   +5 more
europepmc   +1 more source

PNL: a software to build polygenic risk scores using a super learner approach based on PairNet, a Convolutional Neural Network. [PDF]

Bioinformatics
Chen TH, Lee CJ, Chen SP, Wu SJ, Fann CSJ.   +4 more
europepmc   +1 more source

Sustainability and Time Trends in Electronic Patient-Reported Outcome Assessment in Routine Cancer Care: Systematic Scoping Review and Follow-Up Survey.

J Med Internet Res
Hubel NJ, Vorbach SM, de Ligt KM, Rathgeber IS, Beyer K, Wintner LM, Faller B, Nemec J, Holzner B, Sztankay M, Lehmann J.   +10 more
europepmc   +1 more source

Tsbrowse: an interactive browser for Ancestral Recombination Graphs

Karthikeyan S, Jeffery B, Mbuli-Robertson D, Kelleher J.   +3 more
europepmc   +1 more source

ChatGPT for Univariate Statistics: Validation of AI-Assisted Data Analysis in Healthcare Research.

J Med Internet Res
Ruta MR, Gaidici T, Irwin C, Lifshitz J.
europepmc   +1 more source

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Empirical Processes with Applications to Statistics.

The Statistician, 1987
22. Empirical Processes with Applications to Statistics. By G. R. Shorack and J. A. Wellner. ISBN 0 471 86725 X. Wiley, 1986. xxxvii, 938p. £57.45 (Wiley Series in Probability and Mathematical Statistics.)
Dennis D. Boos, Galen R. Shorack, Jon A. Wellner   +2 more
openaire   +2 more sources

Applications in Statistics

2009
Special functions are used in almost all areas of statistics. Statistical densities are basically elementary special functions or product of such functions. Hence, the theory of special functions is directly applicable to statistical distribution theory.
Arak M. Mathai, Hans J. Haubold, Ram K. Saxena   +2 more
openaire   +2 more sources

Statistical Deobfuscation of Android Applications

Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 2016
This work presents a new approach for deobfuscating Android APKs based on probabilistic learning of large code bases (termed "Big Code"). The key idea is to learn a probabilistic model over thousands of non-obfuscated Android applications and to use this probabilistic model to deobfuscate new, unseen Android APKs.
Bichsel, Benjamin, Raychev, Veselin, Tsankov, Petar, Vechev, Martin   +3 more
openaire   +2 more sources