title: Combinatorial optimization models for finding genetic signatures from gene expression datasets.
abstract: 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.
authors: Centre of Bioinformatics, Biomarker Discovery and Information-Based Medicine, The University of Newcastle, Callaghan, New South Wales, Australia.
authors: Regina
authors: R
authors: Berretta
authors: Wagner
authors: W
authors: Costa
authors: Pablo
authors: P
authors: Moscato
mesh_terms: D000544:Alzheimer Disease / Q000235:genetics / Q000378:metabolism; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*