When analyzing biomolecular data, researchers are often confronted with questions such as "How many groups are in my data?" or "How robust is the identified grouping?"
Cluster analysis provides the mathematical and algorithmic fundamentals for identifying groups of similar objects. However, performance issues quickly arise when analyzing typical data sets comprising thousands of samples and up to millions of features. We research the adaptation of clustering approaches to high-dimensional biomolecular data, including parallel cluster algorithms. Furthermore, we develop new methods for the evaluation of clusterings with respect to their stability, such as the combination of multiple cluster validation indices.
J. Kraus, L. Lausser, and H. A. Kestler. Exhaustive k-nearest-neighbour subspace clustering. Journal of Statistical Computation and Simulation, 85(1):30–46, 2015.
J. M. Kraus, C. Müssel, G. Palm, and H. A. Kestler. Multi-objective selection for collecting cluster alternatives. Computational Statistics, 26(2):341–353, 2011.
J. M. Kraus and H. A. Kestler. A highly efficient multi-core algorithm for clustering extremely large datasets. BMC Bioinformatics, 11(1):169, 2010.
H. A. Kestler, J. Kraus, G. Palm, and F. Schwenker. On the effects of constraints in semi-supervised hierarchical clustering. In F. Schwenker and S. Marinai, editors, Artificial Neural Networks in Pattern Recognition (ANNPR 06), volume LNAI 4087, pages 57–66. Springer-Verlag, Heidelberg, 2006.
T. Mattfeldt, H. Wolter, R. Kemmerling, H.-W. Gottfried, and H. A. Kestler. Cluster analysis of comparative genomic hybridization (CGH) data using self-organizing maps: Application to prostate carcinomas. Analytical Cellular Pathology, 23(1):29–37, 2001.
Our first quantum computing paper "Leveraging quantum computing for dynamic analyses of logical networks in systems biology" has been published in Patterns.
Our paper "Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images" has been published in Frontiers in Artificial Intelligence.
"Vaccine Side Effects in Health Care Workers after Vaccination against SARS-CoV-2: Data from TüSeRe:exact Study" has been published in Viruses-Basel.
"PREDICT-juvenile-stroke: PRospective evaluation of a prediction score determining individual clinical outcome three months after ischemic stroke in young adults – a study protocol" has been published in BMC Neurology.
Our paper "Federated Electronic Data Capture (fEDC): Architecture and Prototype" has been accepted for publiaction in the Journal of Biomedical Informatics.
Our paper "Efficient cross-valdation traversals in feature subset selection" has been published in Scientific Reports.
Our paper "CANTATA - prediction of missing links in Boolean networks using genetic programming" has been published in Bioinformatics.
Our paper "Interaction Empowerment in Mobile Health: Concepts, Challenges, and Perspectives" has been published in the Journal of Medical Internet Research mhealth and uhealth.
Our paper "Identification of dynamic driver sets controlling phenotypical landscapes" has been published in the Computational and Structural Biotechnology Journal.
Congratulations to Dr. Silke Werle for winning the 1st Prize with her pitch at the 1. Science Day held by ProTrainU.
Our paper "Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells" has been published in the Computational and Structural Biotechnology Journal.
The position paper "Is there a role for statistics in artificial intelligence" has been published online first in Advances in Data Analysis and Classification.