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