Research
Modern biology is to increasing degree dependent on so
called high-throughput techniques, i.e. massively parallel experiments
that generate a large set of readouts. Examples of such techniques are
shotgun proteomics, yeast two hybrid, micro-arrays and next generation
sequencing. A common challenge for these kinds of experiments is that
the interpretation of the outcomes, as the individual measurements are
of varying quality. I am aiming at increasing the yield and
facilitating the interpretation of high-throughput experiments by
using different machine learning methods such as support vector
machines and dynamical Bayesian networks.