Abstract
We investigate the use of perceptrons for classification of microarray data where we use two datasets that were published in Khan et al., Nature [Medicine], vol. 7, 2001, and Golub et al., Science, vol. 286, 1999. The classification problem studied by Khan et al. is related to the diagnosis of small round blue cell tumours of childhood (SRBCT) which are difficult to classify both clinically and via routine histology. Golub et al. study acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). We used a simulated annealing-based method in learning a system of perceptrons, each obtained by resampling of the training set. Our results are comparable to those of Khan et al. and Golub et al., indicating that there is a role for perceptrons in the classification of tumours based on gene expression data. We also show that it is critical to perform feature selection in this type of models, i.e., we propose a method for identifying genes that might be significant for the particular tumour types. For SRBCTs, zero error on test data has been obtained for only 10 out of 2308 genes; for the ALL/AML problem, our results are competitive to the best results published in the literature, and we obtain 6 genes out of 7129 genes that are used for the classification procedure. Furthermore, we provide evidence that Epicurean-style learning is essential for obtaining the best classification results.
Research partially supported by EPSRC Grant GR/R72938/01 and by the Taplin award from the Harvard/MIT Health Sciences and Technology Division.
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Albrecht, A.A., Vinterbo, S.A., Ohno-Machado, L. (2003). Epicurean-style Learning Applied to the Classification of Gene-Expression Data. In: Bramer, M., Preece, A., Coenen, F. (eds) Research and Development in Intelligent Systems XIX. Springer, London. https://doi.org/10.1007/978-1-4471-0651-7_4
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