Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Lucas Kook"'
Autor:
Marc T. Schmidt, Marc Studer, Andres Kunz, Sandro Studer, John M. Bonvini, Marco Bueter, Lucas Kook, Sarah R. Haile, Andreas Pregernig, Beatrice Beck-Schimmer, Martin Schläpfer
Publikováno v:
BMC Anesthesiology, Vol 23, Iss 1, Pp 1-9 (2023)
Abstract Purpose Carbon dioxide (CO2) increases cerebral perfusion. The effect of CO2 on apnea tolerance, such as after anesthesia induction, is unknown. This study aimed to assess if cerebral apnea tolerance can be improved in obese patients under g
Externí odkaz:
https://doaj.org/article/517ddc8d2b194b44b00e4691059e0b3e
Autor:
David Rügamer, Chris Kolb, Cornelius Fritz, Florian Pfisterer, Philipp Kopper, Bernd Bischl, Ruolin Shen, Christina Bukas, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, Philipp F. M. Baumann, Lucas Kook, Nadja Klein, Christian L. Müller
Publikováno v:
Journal of Statistical Software, Vol 105, Pp 1-31 (2023)
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks. Our implementation encom
Externí odkaz:
https://doaj.org/article/da18f6b48a9a4792973885c91aad0334
Autor:
Klemens Fröhlich, Eva Brombacher, Matthias Fahrner, Daniel Vogele, Lucas Kook, Niko Pinter, Peter Bronsert, Sylvia Timme-Bronsert, Alexander Schmidt, Katja Bärenfaller, Clemens Kreutz, Oliver Schilling
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-13 (2022)
Data independent acquisition (DIA) has been gaining momentum in clinical proteomics. Here, the authors create a benchmark dataset comprising inter-patient heterogeneity to compare popular DIA data analysis workflows for identifying differentially abu
Externí odkaz:
https://doaj.org/article/17f2eb2214c94026859e0c88b1eb7fa8
Autor:
Vanessa Drendel, Bianca Heckelmann, Christoph Schell, Lucas Kook, Martin L. Biniossek, Martin Werner, Cordula A. Jilg, Oliver Schilling
Publikováno v:
Clinical Proteomics, Vol 15, Iss 1, Pp 1-15 (2018)
Abstract Background Renal oncocytomas (ROs) are benign epithelial tumors of the kidney whereas chromophobe renal cell carcinoma (chRCCs) are malignant renal tumors. The latter constitute 5–7% of renal neoplasias. ROs and chRCCs show pronounced mole
Externí odkaz:
https://doaj.org/article/5867f99d72bb405697d2d9141229517c
Publikováno v:
Proteomes, Vol 9, Iss 2, p 26 (2021)
Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become the most commonly used technique in explorative proteomic research. A variety of open-source tools for peptide-spectrum matching have become available. Most analyses of explorative
Externí odkaz:
https://doaj.org/article/bef804db7f3c420a9bfbe3cf3e6296b9
Autor:
Fernando Marmolejo‐Ramos, Mauricio Tejo, Marek Brabec, Jakub Kuzilek, Srecko Joksimovic, Vitomir Kovanovic, Jorge González, Thomas Kneib, Peter Bühlmann, Lucas Kook, Guillermo Briseño‐Sánchez, Raydonal Ospina
Publikováno v:
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13 (1)
The advent of technological developments is allowing to gather large amounts of data in several research fields. Learning analytics (LA)/educational data mining has access to big observational unstructured data captured from educational settings and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b06653340d9acd0ad1a22a1e7cb6c198
First update of the preprint
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::40c66a599a9baa50a1d9de2f5b1c867f
We introduce a generalized additive model for location, scale, and shape (GAMLSS) next of kin aiming at distribution-free and parsimonious regression modelling for arbitrary outcomes. We replace the strict parametric distribution formulating such a m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e71725b36f9b216536f7b55110671982
http://arxiv.org/abs/2208.05302
http://arxiv.org/abs/2208.05302
Publikováno v:
Statistics and Computing, 32 (3)
Prediction models often fail if train and test data do not stem from the same distribution. Out-of-distribution (OOD) generalization to unseen, perturbed test data is a desirable but difficult-to-achieve property for prediction models and in general
This release includes all relevant files to reproduce the arXiv preprint (https://arxiv.org/abs/2203.13076).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7f5f949695694ded041bb956fecfa0d2