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 and massively parallel sequencing. A common challenge for these kinds of experiments is that the interpretation of the outcomes, as the individual measurements are of varying quality. We are 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.