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Decision Making Association Rules for Recognition of Differential Gene Expression Profiles

  • C. Rubio-Escudero
  • Coral del Val
  • O. Cordón
  • I. Zwir
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

The rapid development of methods that select over/under expressed genes from RNA microarray experiments have not yet satisfied the need for tools that identify differential profiles that distinguish between experimental conditions such as time, treatment and phenotype. We evaluate several microarray analysis methods and study their performance, finding that none of the methods alone identifies all observable differential profiles, nor subsumes the results obtained by the other methods. Therefore, we propose a machine learning based methodology that identifies and combines the abilities of microarray analysis methods to recognize differential profiles. We encode the results of this methodology in decision making association rules able to decide which method or method-aggregation is optimal to retrieve a set of genes exhibiting a common profile. These solutions are optimal in the sense that they constitute partial ordered subsets of all method-aggregations bounded by the most specific and the most sensitive available solution. This methodology was successfully applied to a study of inflammation and host response to injury data set derived from the analysis of longitudinal blood microarray profiles of human volunteers treated with intravenous endotoxin compared to placebo. Our approach was able to uncover a cohesive set of differentially expressed genes and novel members exhibiting previously studied differential profiles. This guideline serves as a means to support decisions on new microarray problems.

Keywords

Association Rule Differential Gene Expression Multiobjective Optimization Microarray Gene Expression Data Differential Expression Profile 
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
  • Coral del Val
    • 1
  • O. Cordón
    • 1
    • 2
  • I. Zwir
    • 1
    • 3
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of GranadaSpain
  2. 2.European Center for Soft ComputingMieresSpain
  3. 3.Howard Hughes Medical InstituteWashington University School of MedicineSt. Louis

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