Proteomics is a relatively young discipline dealing with the complicated protein mixture in biological systems, for example the human body. Changes in the concentration of some protein species can often be directly linked to diseases, such as cancer. In this project we develop algorithms capable of identifying changes in the very high dimensional proteome data we are getting from clinical partners.
In particular, we are interested in tiny signals that are usually hidden in the noisy part of the data and not detectable by current approaches. Nevertheless, these very small signals are crucial for reliable classification of biological samples.
Our mathematical research is focussed on the development of a class of specific Bayesian classification schemes that are coupled to novel model discrimination approaches. For all our methods we put special emphasis on the applicability in clinical areas, i.e. the algorithms are designed to be simple, fast and reliable, with particular respect to sensitivity and specificity sufficient for clinical requirements.
The project is funded through grants from Microsoft Research, IBM and the International Max-Planck Research School "Computational Biology and Scientific Computing".
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