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An Immune-Evolutionary Algorithm for Multiple Rearrangements of Gene Expression Data

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Abstract

Microarray technologies are employed to simultaneously measure expression levels of thousands of genes. Data obtained from such experiments allow inference of individual gene functions, help to identify genes from specific tissues, to analyze the behavior of gene expression levels under various environmental conditions and under different cell cycle stages, and to identify inappropriately transcribed genes and several genetic diseases, among many other applications. As thousands of genes may be involved in a microarray experiment, computational tools for organizing and providing possible visualizations of the genes and their relationships are crucial to the understanding and analysis of the data. This work proposes an algorithm based on artificial immune systems for organizing gene expression data in order to simultaneously reveal multiple features in large amounts of data. A distinctive property of the proposed algorithm is the ability to provide a diversified set of high-quality rearrangements of the genes, opening up the possibility of identifying various co-regulated genes from representative graphical configurations of the expression levels. This is a very useful approach for biologists, because several co-regulated genes may exist under different conditions.

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de Sousa, J.S., de C. T. Gomes, L., Bezerra, G.B. et al. An Immune-Evolutionary Algorithm for Multiple Rearrangements of Gene Expression Data. Genet Program Evolvable Mach 5, 157–179 (2004). https://doi.org/10.1023/B:GENP.0000023686.59617.57

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  • DOI: https://doi.org/10.1023/B:GENP.0000023686.59617.57

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