Gene Priorization for Tumor Classification Using an Embedded Method

  • Jose M. Cadenas
  • M. Carmen Garrido
  • Raquel Martínez
  • David Pelta
  • Piero P. Bonissone
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 613)

Abstract

The application of microarray technology to the diagnosis of cancer has been a challenge for computational techniques because the datasets obtained have high dimension and a few examples. In this paper two computational techniques are applied to tumor datasets in order to carry out the task of diagnosis of cancer (classification task) and identifying the most promising candidates among large list of genes (gene prioritization). Both techniques obtain good classification results but only one provides a ranking of genes as additional information and thus, more interpretable models, being more suitable for jointly addressing both tasks.

Keywords

Fuzzy random forest Gene priorization Gene expression data Tumor datasets 

Notes

Acknowledgments

Supported by the projects TIN2011-27696-C02-01 and TIN2011-27696-C02-02 of the Ministry of Economy and Competitiveness of Spain. Thanks also to “Agencia de Ciencia y Tecnología de la Región de Murcia” (Spain) for the support given to Raquel Martínez by the scholarship program FPI.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jose M. Cadenas
    • 1
  • M. Carmen Garrido
    • 1
  • Raquel Martínez
    • 1
  • David Pelta
    • 2
  • Piero P. Bonissone
    • 3
  1. 1.Department of Information Engineering and CommunicationsUniversity of MurciaMurciaSpain
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain
  3. 3.General Electric Global ResearchOne Research CircleNiskayunaU.S.A.

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