Edmund S. Jackson

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This paper will give a complete methodological approach to the processing of oligonucleotide microarray data from postmortem tissue, particularly brain matter. Attention will be drawn to each of the important stages in the process; specifically the quality control, gene expression value calculation, multiple hypothesis testing and correlation analyses. We(More)
This paper presents a Bayesian technique aimed at classifying signals without prior training (clustering). The approach consists of modelling the observed signals, known only through a finite set of samples corrupted by noise, as Gaussian processes. As in many other Bayesian clustering approaches, the clusters are defined thanks to a mixture model. In order(More)
MOTIVATION A significant and stubbornly intractable problem in genome sequence analysis has been the de novo identification of transcription factor binding sites in promoter regions. Although theoretically pleasing, probabilistic methods have faced difficulties due to model mismatch and the nature of the biological sequence. These problems result in(More)
This technical report presents a novel algorithm for unsupervised clustering of functions. It proceeds by developing the theory of unsupervised classification in mixtures from the familiar mixture of Gaussian distributions, to the infinite mixture of Gaussian processes. At each stage a both a theoretical and an algorithmic exposition are presented. We(More)
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