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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References
Bishop, C.M., Svensen, M., Williams, C.K.I. (1998). GTM: The generative topographic mapping. Neural Computation.
Brown, M., Grundy, W., Lin, D., Cristianini, N., Sugnet, C., Furey, T., Ares, M. Jr., Haussler, D. (2000). Knowledge-based Analysis of Microarray Gene Expression Data by Using Support Vector Machines. Proceedings of the National Academy of Science USA, 97(1), 262–267.
Chang, K., Ghosh, J. (2001). A unified model for probabilistic principal surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23,(1).
Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. PNAS, 95:14863–14868
Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D., Futcher, B. (1998). Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell, 9:3273–3297.
Staiano, A. (2003). Unsupervised Neural Networks for the Extraction of Scientific Information from Astronomical Data. PhD thesis, Università di Salerno, Italy.
Staiano, A., Tagliaferri, R., De Vinco, L., Longo, G. (2004). High-D Data Visualization Methods via Probabilistic Principal Surfaces for Data Mining Applications. In Chang, S.K. (Ed) Multimedia Databases and Image Communication, Salerno. Series on Software Engineering and Knowledge Engineering — World Scientific — in press.
Tagliaferri, R., Ciaramella, A., Milano, L., Barone, F., Longo, G. (1999). Spectral analysis of stellar light curves by means of neural networks. Astronomy and Astrophysics Supplement Series, 137:391–405.
Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E.S., Golub, T.R. (1999). Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. PNAS, 96:2907–2912.
Toronen, P., Kolehmainen, M., Wong, G., Castren, E. (1999). Analysis of gene expression data using self-organizing maps. FEBS Letters 451:142–146.
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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
Online ISBN: 978-1-4020-3432-9
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