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Bioinformatics

Volume 453 of the series Methods in Molecular Biology™ pp 363-377

Combinatorial Optimization Models for Finding Genetic Signatures from Gene Expression Datasets

  • Regina BerrettaAffiliated withCentre of Bioinformatics, Biomarker Discovery and Information-Based Medicine, The University of Newcastle
  • , Wagner CostaAffiliated withSchool of Electrical Engineering and Computer Science, The University of Newcastle
  • , Pablo MoscatoAffiliated withARC Centre of Excellence in Bioinformatics, and Centre of Bioinformatics, Biomarker Discovery and Information-Based Medicine, The University of Newcastle

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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.

Key words:

Combinatorial optimization integer programming gene selection feature selection gene ordering microarray data analysis Alzheimer's disease.