Computational intelligence techniques for acute leukemia gene expression data classification

Recent advances in microarray technologies have allowed scientists to discover and monitor the mRNA transcript levels of thousands of genes in a single experiment. The data obtained from microarray studies present a challenge to data analysis. In this paper, we design an expression-based classification method for acute leukemia. Different dimension reduction techniques are considered to tackle the very high dimensionality of this kind of data. Subsequently, the classification system employs artificial neural networks. The comparative results reported, indicate that high classification rates are possible and moreover that subsets of features that contribute significantly to the success of the neural classifiers can be identified.