Genes Related with Alzheimer's Disease: A Comparison of Evolutionary Search, Statistical and Integer Programming Approaches

P. Moscato, R. Berretta, M. Hourani, A. Mendes, C. Cotta

Applications of Evolutionary Computation, F. Rothlauf et al.(eds.), Lecture Notes in Computer Science 3449, pp. 84-94, Springer-Verlag Berlin, 2005

© Springer-Verlag Berlin Heidelberg 2005. All rights reserved.


Three different methodologies have been applied to microarray data from brains of Alzheimer diagnosed patients and healthy patients taken as control. A clear pattern of differential gene expression results which can be regarded as a molecular signature of the disease. The results show the complementarity of the different methodologies, suggesting that a unified approach may help to uncover complex genetic risk factors not currently discovered with a single method. We also compare the set of genes in these differential patterns with those already reported in the literature.

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