Optimal Selection of Microarray Analysis Methods Using a Conceptual Clustering Algorithm

  • C. Rubio-Escudero
  • R. Romero-Záliz
  • O. Cordón
  • O. Harari
  • C. del Val
  • I. Zwir
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)


The rapid development of methods that select over/under expressed genes from microarray experiments have not yet matched the need for tools that identify informational profiles that differentiate between experimental conditions such as time, treatment and phenotype. Uncertainty arises when methods devoted to identify significantly expressed genes are evaluated: do all microarray analysis methods yield similar results from the same input dataset? do different microarray datasets require distinct analysis methods?. We performed a detailed evaluation of several microarray analysis methods, finding that none of these methods alone identifies all observable differential profiles, nor subsumes the results obtained by the other methods. Consequently, we propose a procedure that, given certain user-defined preferences, generates an optimal suite of statistical methods. These solutions are optimal in the sense that they constitute partial ordered subsets of all possible method-associations bounded by both, the most specific and the most sensitive available solution.


Association Rule Microarray Experiment Multiobjective Optimization Microarray Dataset Pareto Optimal Front 
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 2006

Authors and Affiliations

  • C. Rubio-Escudero
    • 1
  • R. Romero-Záliz
    • 1
  • O. Cordón
    • 1
  • O. Harari
    • 1
  • C. del Val
    • 1
  • I. Zwir
    • 1
    • 2
  1. 1.Department of Computer Science and Artificial IntelligenceGranadaSpain
  2. 2.Howard Hughes Medical InstituteWashington University School of MedicineSt. Louis

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