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Benchmarking a memetic algorithm for ordering microarray data

Authors
Journal
Biosystems
0303-2647
Publisher
Elsevier
Publication Date
Volume
88
Identifiers
DOI: 10.1016/j.biosystems.2006.04.005
Keywords
  • Memetic Algorithms
  • Tabu Search
  • Gene Ordering
  • Clustering
  • Microarray
Disciplines
  • Biology
  • Computer Science

Abstract

Abstract This work introduces a new algorithm for “gene ordering”. Given a matrix of gene expression data values, the task is to find a permutation of the gene names list such that genes with similar expression patterns should be relatively close in the permutation. The algorithm is based on a combined approach that integrates a constructive heuristic with evolutionary and Tabu Search techniques in a single methodology. To evaluate the benefits of this method, we compared our results with the current outputs provided by several widely used algorithms in functional genomics. We also compared the results with our own hierarchical clustering method when used in isolation. We show that the use of images, corrupted with known levels of noise, helps to illustrate some aspects of the performance of the algorithms and provide a complementary benchmark for the analysis. The use of these images, with known high-quality solutions, facilitates in some cases the assessment of the methods and helps the software development, validation and reproducibility of results. We also propose two quantitative measures of performance for gene ordering. Using these measures, we make a comparison with probably the most used algorithm (due to Eisen and collaborators, PNAS 1998) using a microarray dataset available on the public domain (the complete yeast cell cycle dataset).

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