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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/804485

Title: Combinatorial optimization models for finding genetic signatures from gene expression datasets
Author/Creator: Berretta, Regina; Costa, Wagner; Moscato, Pablo
Institution: The University of Newcastle. Faculty of Engineering & Built Environment, School of Electrical Engineering and Computer Science
Description: 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 ienes, 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.
Relation: Bioinformatics, Volume 2. p. 363-377
Relation: Methods in Molecular Biology 453
Publisher Link: http://dx.doi.org/10.1007/978-1-60327-429-6
Date: 2008
Publisher: Humana Press
Keyword(s): combinatorial optimization; integer programming; gene selection; feature selection; gene ordering; microarray data analysis; Alzheimer's disease
Resource Type: book chapter
Identifier: http://hdl.handle.net/1959.13/804485
Identifier: ISBN:9781603274289

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