Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Riccardo De Bin"'
Autor:
Jörg Rahnenführer, Riccardo De Bin, Axel Benner, Federico Ambrogi, Lara Lusa, Anne-Laure Boulesteix, Eugenia Migliavacca, Harald Binder, Stefan Michiels, Willi Sauerbrei, Lisa McShane, for topic group “High-dimensional data” (TG9) of the STRATOS initiative
Publikováno v:
BMC Medicine, Vol 21, Iss 1, Pp 1-54 (2023)
Abstract Background In high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many mea
Externí odkaz:
https://doaj.org/article/4341909edf664733b9e0a8f2c0eedbe3
Publikováno v:
BMC Bioinformatics, Vol 24, Iss 1, Pp 1-23 (2023)
Abstract Background The research of biomarker-treatment interactions is commonly investigated in randomized clinical trials (RCT) for improving medicine precision. The hierarchical interaction constraint states that an interaction should only be in a
Externí odkaz:
https://doaj.org/article/6d55fabc0d1942cc8579637748337697
Publikováno v:
Fractal and Fractional, Vol 7, Iss 9, p 641 (2023)
We propose a framework for fitting multivariable fractional polynomial models as special cases of Bayesian generalized nonlinear models, applying an adapted version of the genetically modified mode jumping Markov chain Monte Carlo algorithm. The univ
Externí odkaz:
https://doaj.org/article/398053d8dea242bd898944fbbab3bea7
Publikováno v:
BMC Bioinformatics, Vol 21, Iss 1, Pp 1-20 (2020)
Abstract Background The standard lasso penalty and its extensions are commonly used to develop a regularized regression model while selecting candidate predictor variables on a time-to-event outcome in high-dimensional data. However, these selection
Externí odkaz:
https://doaj.org/article/38326abb1e6e40fa92ea4f297801797a
Publikováno v:
BMC Medical Research Methodology, Vol 19, Iss 1, Pp 1-15 (2019)
Abstract Background Omics data can be very informative in survival analysis and may improve the prognostic ability of classical models based on clinical risk factors for various diseases, for example breast cancer. Recent research has focused on inte
Externí odkaz:
https://doaj.org/article/0728cee7970b4b37962ce371f576b630
Publikováno v:
PLoS ONE, Vol 15, Iss 11, p e0242334 (2020)
The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. D
Externí odkaz:
https://doaj.org/article/eb15be37c09c43bd9a3b0f95a007a2df
Publikováno v:
Physical Review Physics Education Research, Vol 17, Iss 2, p 020104 (2021)
Across the field of education research there has been an increased focus on the development, critique, and evaluation of statistical methods and data usage due to recently created, very large datasets and machine learning techniques. In physics educa
Externí odkaz:
https://doaj.org/article/214d97928e824b449e4598b59d307920
Autor:
Hannes Kneiding, Ruslan Lukin, Lucas Lang, Simen Reine, Thomas Bondo Pedersen, Riccardo De Bin, David Balcells
Publikováno v:
Digital Discovery.
Machine learning can make a strong contribution to accelerating the discovery of transition metal complexes (TMC). These compounds will play a key role in the development of new technologies for which there is an urgent need, including the production
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5df85c5557c66eac0c9d92a1d6d92f8a
https://doi.org/10.1039/d2dd00129b
https://doi.org/10.1039/d2dd00129b
Autor:
Pascal Friederich, Alán Aspuru-Guzik, David Balcells, Riccardo De Bin, Gabriel dos Passos Gomes
Publikováno v:
Chemical Science
A machine learning exploration of the chemical space surrounding Vaska's complex.
Homogeneous catalysis using transition metal complexes is ubiquitously used for organic synthesis, as well as technologically relevant in applications such as wate
Homogeneous catalysis using transition metal complexes is ubiquitously used for organic synthesis, as well as technologically relevant in applications such as wate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b6e4d32aa254908c92a200e6a3ae531
https://doi.org/10.1039/d0sc00445f
https://doi.org/10.1039/d0sc00445f
In this paper we propose a boosting algorithm to extend the applicability of a first hitting time model to high-dimensional frameworks. Based on an underlying stochastic process, first hitting time models do not require the proportional hazards assum
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b770f54f6df567d1b5b08085d84b8738
http://hdl.handle.net/10852/93844
http://hdl.handle.net/10852/93844
