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Medical Systems Biology

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Bioinformatics

The advent of high-throughput biomolecular technologies has made high-dimensional biological data available for the investigation of many clinical settings. The large numbers of features and low numbers of probes in such data sets poses many challenges for their analysis. Machine learning approaches and statistical methods are essential for the interpretation of the data. For example, clustering methods can detect groups of similar probes. Feature selection techniques are employed to identify features (e.g. marker genes) that are relevant to distinguish certain phenotypes. Classification algorithms can predict the phenotype of a probe according to the measurements.

Latest News

 

Our paper "Capturing dynamic relevance in Boolean networks using graph theoretical measures" has been published in Bioinformatics.

 

Our paper "Supporting Medical Staff from Psycho-Oncology with Smart Mobile Devices: Insights into the Development Process and First Results" has been published in the International Journal of Environmental Research and Public Health.

 

Our paper "Unraveling the Molecular Tumor-Promoting Regulation of Cofilin-1 in Pancreatic Cancer" has been published in MDPI Cancers.

 

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 Cancers.