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.
Wissenschaftlicher Mitarbeiter (m/w/d)
"Recent Trends and Future Challenges in Learning from Data" has been published with Springer.
Our paper "Permutation-invariant linear classifiers" has been published in Machine Learning.
Our paper "Prediction of resistance to bevacizumab plus FOLFOX in metastatic colorectal cancer-Results of the prospective multicenter PERMAD trial" has been published in PLoS One.
Our paper "Segmentation-based cardiomegaly detection based on semi-supervised estimation of cardiothoracic ratio" has been published in Scientific Reports.
"Prospective study validating a multidimensional treatment decision score predicting the 24-month outcome in untreated patients with clinically isolated syndrome and early relapsing–remitting multiple sclerosis, the ProVal-MS study" has been published in Neurological Research and Practice.
Our paper "GatekeepR: an R shiny application for the identification of nodes with high dynamic impact in boolean networks" has been published online first in Bioinformatics.
Our paper "The Necessity of Interoperability to Uncover the Full Potential of Digital Health Devices" has been published in JMIR Medical Informatics.
"Multicentric pilot study to standardize clinical whole exome sequencing (WES) for cancer patients" has been published in npj Precision Oncology.
Our paper "AMBAR-interactive alteration annotations for molecular tumor boards" has been published in Computer Methods and Programs in Biomedicine.
"A protocol for the use of cloud-based quantum computers for logical network analysis of biological systems" has been published in STAR Protocols.
Our paper "A systems biology approach to define mechanisms, phenotypes, and drivers in PanNETs with a personalized perspective" has been published in npj systems biology and applications.
"Supporting SURgery with GEriatric Co-Management and AI (SURGE-Ahead): A study protocol for the development of a digital geriatrician" has been published in PLoS One.
"Self-Assessment of Having COVID-19 With the Corona Check Mhealth App" has been published in IEEE Journal of Biomedical and Health Informatics.
Our first quantum computing paper "Leveraging quantum computing for dynamic analyses of logical networks in systems biology" has been published in Patterns.