Statistical Biotechnology

Photo by Yrin Eldfjell
Statistical Biotechnology, from left to right: Matthew The, Yunyi Zhu, XuanBin Qiu, Yrin Eldfjell, Lukas Käll, and Heydar Maboudi Afkham

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.