Abstract
Gene-expression microarrays make it possible to simultaneously measure the rate at which a cell or tissue is expressing each of its thousands of genes. One can use these comprehensive snapshots of biological activity to infer regulatory pathways in cells, identify novel targets for drug design, and improve diagnosis, prognosis, and treatment planning for those suffering from disease. However, the amount of data this new technology produces is more than one can manually analyze. Hence, the need for automated analysis of microarray data offers an opportunity for machine learning to have a significant impact on biology and medicine. Probabilistic Principal Surfaces defines a unified theoretical framework for nonlinear latent variable models embracing the Generative Topographic Mapping as a special case. This article describes the use of PPS for the analysis of yeast gene expression levels from microarray chips showing its effectiveness for high-D data visualization and clustering.
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Staiano, A. et al. (2005). Mining Yeast Gene Microarray Data with Latent Variable Models. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Biological and Artificial Intelligence Environments. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3432-6_10
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DOI: https://doi.org/10.1007/1-4020-3432-6_10
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3431-2
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