Hierarchic Bayesian models for kernel learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Microarray image gridding with stochastic search based approaches
Image and Vision Computing
Comments on selected fundamental aspects of microarray analysis
Computational Biology and Chemistry
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Motivation: Gene expression levels are obtained from microarray experiments through the extraction of pixel intensities from a scanned image of the slide. It is widely acknowledged that variabilities can occur in expression levels extracted from the same images by different users with the same software packages. These inconsistencies arise due to differences in the refinement of the placement of the microarray 'grids'. We introduce a novel automated approach to the refinement of grid placements that is based upon the use of Bayesian inference for determining the size, shape and positioning of the microarray 'spots', capturing uncertainty that can be passed to downstream analysis. Results: Our experiments demonstrate that variability between users can be significantly reduced using the approach. The automated nature of the approach also saves hours of researchers' time normally spent in refining the grid placement. Availability: A MATLAB implementation of the algorithm and tiff images of the slides used in our experiments, as well as the code necessary to recreate them are available for non-commercial use from http://www.dcs.shef.ac.uk/~neil/VIS