Our research deals with computational methods for analyzing and understanding biological data, focused on the two areas of bioimaging and noncoding RNomics. We consider our work as algorithmic modeling: the development of algorithms and computational protocols goes hand-in-hand with modeling the underlying biological systems and structures.
In our research, we apply existing methods and develop new algorithms using a diverse range of techniques from pattern recognition, combinatorial optimization and machine learning.
Biological imaging technology has been developing at rapid pace in recent years and was a driving force behind numerous insights into biological systems. Imaging technology has been driven by diverse enhancements of conventional microscopy in terms of spatial and temporal resolution, dimensionality, or multispectral techniques. The widespread availability of novel imaging technologies imposes major challenges to turn illustrative images into quantifiable scientific measurements. In this context, we develop algorithms for analyzing vibrational microspectroscopic images as well as conventional staining-based images.
With the availability of genomes for numerous of organisms, it has become obvious that many RNA transcripts do not code for protein, yet perform essential and in some cases even enzymatic functions in the cell. Our research deals with computational approaches to identify such non-coding RNA genes in genomes and transcriptomes.
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Last update: Apr 11, 2024
