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Knowledge Discovery in Lymphoma Cancer from Gene–Expression

  • Jesús S. Aguilar-Ruiz
  • Francisco Azuaje
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3177)

Abstract

A comprehensive study of the database used in Alizadeh et al. [7], about the identification of lymphoma cancer subtypes within Diffuse Large B–Cell Lymphoma (DLBCL), is presented in this paper, focused on both the feature selection and classification tasks. Firstly, we tackle with the identification of relevant genes in the prediction of lymphoma cancer types, and lately the discovering of most relevant genes in the Activated B–Like Lymphoma and Germinal Centre B–Like Lymphoma subtypes within DLBCL. Afterwards, decision trees provide knowledge models to predict both types of lymphoma and subtypes within DLBCL. The main conclusion of our work is that the data may be insufficient to exactly predict lymphoma or even extract functionally relevant genes.

Keywords

Decision Tree Feature Selection Acute Lymphocytic Leukemia Feature Selection Method Relevant Gene 
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 2004

Authors and Affiliations

  • Jesús S. Aguilar-Ruiz
    • 1
  • Francisco Azuaje
    • 2
  1. 1.Department of Computer ScienceUniversity of SevilleSpain
  2. 2.School of Computing and MathematicsUniversity of Ulster 

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