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The development of microarray technology made possible the expression level measurement of hundreds or even thousands of genes simultaneously for various experimental conditions. The huge amount of available data generated the need for computational methods that allow its analysis in an efficient and automated way. In many of the computational methods employed during gene expression data analysis the choice of a proximity measure is necessary. Among the proximity measures available, correlation coefficients have been widely employed because of their ability to capture similarity trends among the compared numeric sequences (genes or samples). The present work has as objective to compare different correlation measures for the three major tasks involved in the analysis of gene expression data: clustering, feature selection and classification. To this extent, in this dissertation an overview of gene expression data analysis and the different correlation measures considered for this comparison are presented. In the present work are also presented empirical results obtained from the comparison of correlation coefficients for gene clustering, sample clustering, gene selection for sample classification and sample classification.

Estudo de coeficientes de correlação para medidas de proximidade em dados de expressão gênica