Analysing Gene Expression Data Using Gaussian Processes

Complex gene regulatory mechanisms ensure the proper functioning of biological cells. New high-throughput experimental techniques, such as microarrays, provide a snapshot of gene expression levels of thousands of genes at the same time. If repeated on a sample of synchronized cells, time-series profiles of gene activity can be obtained. The aim is to reconstruct the complex gene regulatory network underlying these profiles. Genes often influence each other in a nonlinear fashion and with intricate interaction patterns. Linear models are often unsuited to capture such relationships. Gaussian processes, on the other hand, are ideal for representing nonlinear relationships. A particular attraction is the automatic relevance determination effect, removing unused inputs and resulting in sparse gene networks.