Abstract
The analysis of gene expression data involves the observation of a very large number of variables (genes) on a few units (tissues). In such a context the recourse to conventional classification methods may be hard both for analytical and interpretative reasons. In this work a gene selection procedure for classification problems is addressed. The dimensionality reduction is based on the projections of genes along suitable directions obtained by Independent Factor Analysis (IFA). The performances of the proposed procedure are evaluated in the context of both supervised and unsupervised classification problems for different real data sets.
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References
ATTIAS, H. (1999): Independent Factor Analysis. Neural Computation, 11, 803–851.
CALÒ, D.G., GALIMBERTI, G., PILLATI, M. and VIROLI, C. (2005): Variable selection in classification problems: a strategy based on independent component analysis. In: M. Vichi, P. Monari, S. Mignani and A. Montanari (Eds.): New Developments in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization, Springer-Verlag, Berlin, 21–30.
COMON, P. (1994): Independent component analysis, a new concept? Signal Processing, 36, 287–314.
DUDOIT, S., FRIDLYAND, J. and SPEED, T.P. (2002): Comparison of Discrimination Methods for the Classification of Tumors using Gene Expression Data. Journal of the American Statistical Association, 457, 77–87.
GOLUB, T.R., SLONIM, D.K., TAMAYO, P. et al. (1999): Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science, 286, 531–537.
KHAN, J., WEI, J., RINGNER, M. et al. (2001): Classification and Diagnostic Prediction of Cancers Using Gene Expression Profiling and Artificial Neural Networks. Nature Medicine, 7, 673–679.
TIBSHIRANI, R., HASTIE, T., NARASIMHAN, B. and CHU, G. (2002): Diagnosis of Multiple Cancer Types by Shrunken Centroids of Gene Expression, Proceedings of the National Accademy of Sciences, 99, 6567–6572.
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Pillati, M., Viroli, C. (2006). Gene Selection in Classification Problems via Projections onto a Latent Space. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_21
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DOI: https://doi.org/10.1007/3-540-31314-1_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31313-7
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