An Epicurean learning approach to gene-expression data classification

Albrecht, Andreas A., Vinterbo, Staal A. and Ohno-Machado, Lucila (2003) An Epicurean learning approach to gene-expression data classification. Artificial Intelligence in Medicine, 28 (1) . pp. 75-87. ISSN 0933-3657 [Article] (doi:10.1016/S0933-3657(03)00036-8)


We investigate the use of perceptrons for classification of microarray data where we use two datasets that were published in [Nat. Med. 7 (6) (2001) 673] and [Science 286 (1999) 531]. The classification problem studied by Khan et al. is related to the diagnosis of small round blue cell tumours (SRBCT) of childhood 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 13 out of 2308 genes; for the ALL/AML problem, we have zero error for 9 out of 7129 genes that are used for the classification procedure. Furthermore, we provide evidence that Epicurean-style learning and simulated annealing-based search are both essential for obtaining the best classification results.

Item Type: Article
Keywords (uncontrolled): Perceptrons; simulated annealing; gene-expression analysis
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 12402
Depositing User: Andreas Albrecht
Date Deposited: 11 Nov 2013 07:24
Last Modified: 12 Jun 2019 12:40

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