Center for Computational and Theoretical Biology

Superresolution Clustering


We develop novel computational methods for large-scale cluster analysis of localizations from 3D superresolution microscopy such as dSTORM. 


Hierarchical Clustering

We use extensions of density-based clustering algorithms to derive the full hierarchy of clusters in large image volumes, and develop tools for exploration and visualization of clustering in 3D point clouds.

Cluster classification using Machine Learning 

We develop feature descriptors for point clouds based on geometry, morphology or density, and use advanced trainable algorithms to identify different macromolecular substructures in superresolution images.