Summary
We have presented an ANN-based method for classification of gene expression data. This method was successfully applied to the example of classifying SRBCTs into distinct diagnostic categories. The key components of this classification procedure are PCA for dimensional reduction and cross-validation to optimize the training of the classifiers. In addition, we described a way to rank the genes according to their importance for the classification. Random permutation tests were introduced to assess the significance of the classification results. There are other ANN methods that have been used to classify gene expression data (Selaru et al., 2002).
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Ringnér, M., Edén, P., Johansson, P. (2003). Classification of Expression Patterns Using Artificial Neural Networks. In: Berrar, D.P., Dubitzky, W., Granzow, M. (eds) A Practical Approach to Microarray Data Analysis. Springer, Boston, MA. https://doi.org/10.1007/0-306-47815-3_11
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DOI: https://doi.org/10.1007/0-306-47815-3_11
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4020-7260-4
Online ISBN: 978-0-306-47815-4
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