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Analysing Gene Expression Data Using Gaussian Processes

Analysing Gene Expression Data Using Gaussian Processes

This video was recorded at Gaussian Processes in Practice Workshop, Bletchley Park 2006. 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.


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