Bioinformatics Using Computational Intelligence Paradigms

Volume 176 of the series Studies in Fuzziness and Soft Computing pp 119-141


Class Prediction with Microarray Datasets

  • Simon RogersAffiliated withAdvanced Computing Research Centre, University of Bristol, BS8 1TR
  • , Richard D. WilliamsAffiliated withDept. of Paediatric Oncology, Institute of Cancer Research, Sutton, SM2 5NG
  • , Colin CampbellAffiliated withAdvanced Computing Research Centre, University of Bristol, BS8 1TR

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Microarray technology is having a significant impact in the biological and medical sciences and class prediction will play an increasingly important role in the use and interpretation of microarray data. For example, classifiers could be constructed indicating the detailed subtype of a disease, its expected progression and the best treatment strategy. In this chapter we outline the main stages involved in the development of a successful class predictor for microarray datasets, including data normalisation, the different classifiers which can be used, different feature selection strategies and a method for determining how much data is required for a classification task given an initial sample set. We illustrate this process with both public domain datasets and a new dataset for predicting relapse versus non-relapse for a paediatric tumour.