Medical Systems Biology

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Clustering

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?"

Typical cluster analysis workflow


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.

 

Selected publications

 

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.

Latest News

 

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.

 

Our paper "Corona Health - A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic" has been published in the International Journal of Environmental Research and Public Health.

 

Our paper "Patient empowerment during the COVID-19 pandemic: Ensuring safe and fast communication of test results" has been published in the Journal of Medical Internet Research.

 

Our paper "Perspective on mHealth Concepts to Ensure Users’ Empowerment–From Adverse Event Tracking for COVID-19 Vaccinations to Oncological Treatment" has been published in IEEE Access.

  

Our report protocol "Digitalization of adverse event management in oncology to improve treatment outcome—A prospective study protocol" has been published in PLoS One.

 

We are happy we could contribute to Beutel et al (2021) "A prospective Feasibility Trial to Challenge Patient-Derived Pancreatic Cancer Organoids in Predicting Treatment Response" published in MDPI Cancers.