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pro vyhledávání: '"Mayr Andreas"'
Confronted with the challenge of identifying the most suitable metric to validate the merits of newly proposed models, the decision-making process is anything but straightforward. Given that comparing rankings introduces its own set of formidable cha
Externí odkaz:
http://arxiv.org/abs/2408.16009
Autor:
Strömer, Annika, Klein, Nadja, Staerk, Christian, Faschingbauer, Florian, Klinkhammer, Hannah, Mayr, Andreas
Structured additive distributional copula regression allows to model the joint distribution of multivariate outcomes by relating all distribution parameters to covariates. Estimation via statistical boosting enables accounting for high-dimensional da
Externí odkaz:
http://arxiv.org/abs/2406.03900
Autor:
Bauer Jochen, Hechtel Michael, Konrad Christoph, Holzwarth Martin, Mayr Andreas, Schneider Sven, Franke Jörg, Hoffmann Hilko, Zinnikus Ingo, Feld Thomas, Runge Mathias, Hinz Oliver
Publikováno v:
Current Directions in Biomedical Engineering, Vol 6, Iss 3, Pp 384-387 (2020)
In the upcoming years, the internet of things (IoT) will enrich daily life. The combination of artificial intelligence (AI) and highly interoperable systems will bring contextsensitive multi-domain services to reality. This paper describes a concept
Externí odkaz:
https://doaj.org/article/f6902812465941e8b29162edecaa8c5f
Component-wise gradient boosting algorithms are popular for their intrinsic variable selection and implicit regularization, which can be especially beneficial for very flexible model classes. When estimating generalized additive models for location,
Externí odkaz:
http://arxiv.org/abs/2404.08331
Autor:
Sestak, Florian, Schneckenreiter, Lisa, Brandstetter, Johannes, Hochreiter, Sepp, Mayr, Andreas, Klambauer, Günter
Being able to identify regions within or around proteins, to which ligands can potentially bind, is an essential step to develop new drugs. Binding site identification methods can now profit from the availability of large amounts of 3D structures in
Externí odkaz:
http://arxiv.org/abs/2404.07194
Autor:
Schneckenreiter, Lisa, Freinschlag, Richard, Sestak, Florian, Brandstetter, Johannes, Klambauer, Günter, Mayr, Andreas
Graph neural networks (GNNs), and especially message-passing neural networks, excel in various domains such as physics, drug discovery, and molecular modeling. The expressivity of GNNs with respect to their ability to discriminate non-isomorphic grap
Externí odkaz:
http://arxiv.org/abs/2403.04747
Motivated by challenges in the analysis of biomedical data and observational studies, we develop statistical boosting for the general class of bivariate distributional copula regression with arbitrary marginal distributions, which is suited to model
Externí odkaz:
http://arxiv.org/abs/2403.02194
The biological roles of gene sets are used to group them into collections. These collections are often characterized by being high-dimensional, overlapping, and redundant families of sets, thus precluding a straightforward interpretation and study of
Externí odkaz:
http://arxiv.org/abs/2307.16182
Gene set collections are a common ground to study the enrichment of genes for specific phenotypic traits. Gene set enrichment analysis aims to identify genes that are over-represented in gene sets collections and might be associated with a specific p
Externí odkaz:
http://arxiv.org/abs/2207.12184