Analysing Gene Expression Data Using Gaussian Processes

author: Lorenz Wernisch, Birkbeck College, University of London
published: Feb. 25, 2007,   recorded: June 2006,   views: 5218

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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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