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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 "Analysis, identification and visualization of subgroups in genomics" has been published in Briefings in Bioinformatics.

 

Our paper "A perceptually optimised bivariate visualisation scheme for high-dimensional fold-change data" has been published in Advances in Data Analysis and Classification.