Summary
The advent of DNA microarray technology has supplied a large volume of data to many fields like machine learning and data mining. Gene expression profiles are composed of thousands of genes at the same time, representing complex relationships between them. In this context, intelligent support is essential for managing and interpreting this great amount of information. One of the well-known constraints specifically related to microarray data is the large number of genes in comparison with the small number of available experiments. In this situation, the ability of design methods capable of overcoming current limitations of state-of-the-art algorithms is crucial to the development of successful applications. In this chapter we present a flexible framework for the task of feature selection and classification of microarray data. Dimensionality reduction is achieved by the application of a supervised fuzzy pattern algorithm able to reduce and discretize existing gene expression profiles. An informed growing cell structures network is proposed for clustering biological homogeneous experiments starting from the previous simplified microarray data. Experimental results over different data sets containing acute myeloid leukemia profiles show the effectiveness of the proposed method.
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Glez-Peña, D., Díaz, F., Fdez-Riverola, F., Méndez, J.R., Corchado, J.M. (2009). Fuzzy Patterns and GCS Networks to Clustering Gene Expression Data. In: Jin, Y., Wang, L. (eds) Fuzzy Systems in Bioinformatics and Computational Biology. Studies in Fuzziness and Soft Computing, vol 242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89968-6_6
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