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Genetic Algorithms for Gene Expression Analysis

  • Ed Keedwell
  • Ajit Narayanan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2611)

Abstract

The major problem for current gene expression analysis techniques is how to identify the handful of genes which contribute to a disease from the thousands of genes measured on gene chips (microarrays). The use of a novel neural-genetic hybrid algorithm for gene expression analysis is described here. The genetic algorithm identifies possible gene combinations for classification and then uses the output from a neural network to determine the fitness of these combinations. Normal mutation and crossover operations are used to find increasingly fit combinations. Experiments on artificial and real-world gene expression databases are reported. The results from the algorithm are also explored for biological plausibility and confirm that the algorithm is a powerful alternative to standard data mining techniques in this domain.

Keywords

Genetic Algorithm Gene Expression Analysis Gene Expression Data Acute Myeloid Leukaemia Hybrid Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Ed Keedwell
    • 1
  • Ajit Narayanan
    • 1
  1. 1.School of Engineering and Computer ScienceUniversity of ExeterExeter

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