Thomas Villmann
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Explore the profile of Thomas Villmann including associated specialties, affiliations and a list of published articles.
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32
Citations
135
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Recent Articles
1.
van Veen R, Tamboli N, Lovdal S, Meles S, Renken R, de Vries G, et al.
Artif Intell Med
. 2024 Mar;
149:102786.
PMID: 38462286
In machine learning, data often comes from different sources, but combining them can introduce extraneous variation that affects both generalization and interpretability. For example, we investigate the classification of neurodegenerative...
2.
Engelsberger A, Villmann T
Entropy (Basel)
. 2023 Mar;
25(3).
PMID: 36981428
In the field of machine learning, vector quantization is a category of low-complexity approaches that are nonetheless powerful for data representation and clustering or classification tasks. Vector quantization is based...
3.
Wutzler U, Croy I, Anderssen-Reuster U, Bierling A, Dorner S, Hoffmann T, et al.
Z Psychosom Med Psychother
. 2023 Mar;
69(1):56-75.
PMID: 36927321
As part of the quality assurance of inpatient treatment, the severity of the disease and the course of therapy must be mapped. However, there is a high degree of heterogeneity...
4.
Bohnsack K, Kaden M, Abel J, Villmann T
IEEE/ACM Trans Comput Biol Bioinform
. 2022 Jan;
20(1):119-135.
PMID: 34990369
The encounter of large amounts of biological sequence data generated during the last decades and the algorithmic and hardware improvements have offered the possibility to apply machine learning techniques in...
5.
Bohnsack K, Kaden M, Abel J, Saralajew S, Villmann T
Entropy (Basel)
. 2021 Oct;
23(10).
PMID: 34682081
In the present article we propose the application of variants of the mutual information function as characteristic fingerprints of biomolecular sequences for classification analysis. In particular, we consider the resolved...
6.
Zoghlami F, Kaden M, Villmann T, Schneider G, Heinrich H
Sensors (Basel)
. 2021 Jul;
21(13).
PMID: 34199090
Sensor fusion has gained a great deal of attention in recent years. It is used as an application tool in many different fields, especially the semiconductor, automotive, and medical industries....
7.
Kaden M, Bohnsack K, Weber M, Kudla M, Gutowska K, Blazewicz J, et al.
Neural Comput Appl
. 2021 May;
34(1):67-78.
PMID: 33935376
Supplementary Information: The online version contains supplementary material available at 10.1007/s00521-021-06018-2.
8.
Kudla M, Gutowska K, Synak J, Weber M, Bohnsack K, Lukasiak P, et al.
Bioinformatics
. 2020 Dec;
36(22-23):5507-5513.
PMID: 33367605
Motivation: Viruses are the most abundant biological entities and constitute a large reservoir of genetic diversity. In recent years, knowledge about them has increased significantly as a result of dynamic...
9.
Villmann J, Burkhardt R, Teren A, Villmann T, Thiery J, Drogies T
Thromb Res
. 2019 Jul;
180:98-104.
PMID: 31276978
Introduction: Little is known about peril constellations in primary hemostasis contributing to an acute myocardial infarction (MI) in patients with already manifest atherosclerosis. The study aimed to establish a predicting...
10.
Bittrich S, Kaden M, Leberecht C, Kaiser F, Villmann T, Labudde D
BioData Min
. 2019 Jan;
12:1.
PMID: 30627219
Background: Machine learning strategies are prominent tools for data analysis. Especially in life sciences, they have become increasingly important to handle the growing datasets collected by the scientific community. Meanwhile,...