The aim of this chapter is to present combinatorial optimization models and techniques for the analysis of microarray datasets. The chapter illustrates the application of a novel objective function that guides the search for high-quality solutions for sequential ordering of expression profiles. The approach is unsupervised and a metaheuristic method (a memetic algorithm) is used to provide high-quality solutions. For the problem of selecting discriminative groups of genes, we used a supervised method that has provided good results in a variety of datasets. This chapter illustrates the application of these models in an Alzheimer's disease microarray dataset. © 2008 Humana Press, a part of Springer Science+Business Media, LLC.
CITATION STYLE
Berretta, R., Costa, W., & Moscato, P. (2008). Combinatorial optimization models for finding genetic signatures from gene expression datasets. Methods in Molecular Biology, 453, 363–377. https://doi.org/10.1007/978-1-60327-429-6_19
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