Zobrazeno 1 - 10
of 197
pro vyhledávání: '"Alfred Ultsch"'
Autor:
Alfred Ultsch, Jörg Hoffmann, Maximilian A. Röhnert, Malte von Bonin, Uta Oelschlägel, Cornelia Brendel, Michael C. Thrun
Publikováno v:
BioMedInformatics, Vol 4, Iss 1, Pp 197-218 (2024)
Typical state-of-the-art flow cytometry data samples typically consist of measures of 10 to 30 features of more than 100,000 cell “events”. Artificial intelligence (AI) systems are able to diagnose such data with almost the same accuracy as human
Externí odkaz:
https://doaj.org/article/ad9a48e9309b4116a54214ceb77b1f04
Autor:
Samuel Rischke, Sorwe Mojtahed Poor, Robert Gurke, Lisa Hahnefeld, Michaela Köhm, Alfred Ultsch, Gerd Geisslinger, Frank Behrens, Jörn Lötsch
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-16 (2023)
Abstract Psoriatic arthritis (PsA) is a chronic inflammatory systemic disease whose activity is often assessed using the Disease Activity Score 28 (DAS28-CRP). The present study was designed to investigate the significance of individual components wi
Externí odkaz:
https://doaj.org/article/821a09d2f9b24706ab9ff1d94ec2c98d
Autor:
Jörn Lötsch, Alfred Ultsch
Publikováno v:
BioMedInformatics, Vol 3, Iss 4, Pp 869-884 (2023)
Recent advances in mathematical modeling and artificial intelligence have challenged the use of traditional regression analysis in biomedical research. This study examined artificial data sets and biomedical data sets from cancer research using binom
Externí odkaz:
https://doaj.org/article/8a27d3713a5247ce8ba2ba37d382e3d5
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-14 (2023)
Abstract Random walks describe stochastic processes characterized by a sequence of unpredictable changes in a random variable with no correlation to past changes. This report describes the random walk component of a clinical sensory test of olfactory
Externí odkaz:
https://doaj.org/article/cf9cc8e3ed224d908c63d1982005e585
Publikováno v:
Biomedicines, Vol 12, Iss 8, p 1639 (2024)
Background: Fold change is a common metric in biomedical research for quantifying group differences in omics variables. However, inconsistent calculation methods and inadequate reporting lead to discrepancies in results. This study evaluated various
Externí odkaz:
https://doaj.org/article/6111bad6fc584f979d1ad191bc9c6068
Autor:
Jörn Lötsch, Alfred Ultsch
Publikováno v:
Informatics in Medicine Unlocked, Vol 50, Iss , Pp 101573- (2024)
Background: Clustering on projected data is common in biomedical research analysis. Principal component analysis (PCA) is widely used for projection, focusing on data dispersion (variance), while clustering identifies data concentrations (neighborhoo
Externí odkaz:
https://doaj.org/article/65010c4faca54387ad9a50b672bcb3d9
Autor:
Jörn Lötsch, Alfred Ultsch
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-18 (2023)
Abstract Selecting the k best features is a common task in machine learning. Typically, a few features have high importance, but many have low importance (right-skewed distribution). This report proposes a numerically precise method to address this s
Externí odkaz:
https://doaj.org/article/1b689af6420d4cb6803450b62d4d207f
Autor:
Joerg Hoffmann, Semil Eminovic, Christian Wilhelm, Stefan W. Krause, Andreas Neubauer, Michael C. Thrun, Alfred Ultsch, Cornelia Brendel
Publikováno v:
Current Oncology, Vol 30, Iss 2, Pp 1903-1915 (2023)
Background: The International Prognostic Index (IPI) is applied to predict the outcome of chronic lymphocytic leukemia (CLL) with five prognostic factors, including genetic analysis. We investigated whether multiparameter flow cytometry (MPFC) data o
Externí odkaz:
https://doaj.org/article/2b7e7727681f4d409ecf369ee85f7498
Autor:
Jörn Lötsch, Alfred Ultsch
Publikováno v:
BioMedInformatics, Vol 2, Iss 4, Pp 701-714 (2022)
Feature selection is a common step in data preprocessing that precedes machine learning to reduce data space and the computational cost of processing or obtaining the data. Filtering out uninformative variables is also important for knowledge discove
Externí odkaz:
https://doaj.org/article/ae295651a98a4097b27fff4e50960e59
Autor:
Alfred Ultsch, Jörn Lötsch
Publikováno v:
BMC Bioinformatics, Vol 23, Iss 1, Pp 1-18 (2022)
Abstract Background Data transformations are commonly used in bioinformatics data processing in the context of data projection and clustering. The most used Euclidean metric is not scale invariant and therefore occasionally inappropriate for complex,
Externí odkaz:
https://doaj.org/article/3c1bf93e4a7e46b6ab5fd8859d6dc32d