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Evolutionary Algorithm for Feature Subset Selection in Predicting Tumor Outcomes Using Microarray Data

  • Qihua Tan
  • Mads Thomassen
  • Kirsten M. Jochumsen
  • Jing Hua Zhao
  • Kaare Christensen
  • Torben A. Kruse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4983)

Abstract

Feature subset selection for outcome prediction is a critical issue in large scale microarray experiments in cancer research. This paper introduces an integrative approach that combines significant gene expression analysis, the genetic algorithm and machine learning for selecting informative gene markers and for predicting tumor outcomes including survival outcomes. In case of survival data, full use of individual’s survival information (both censored and uncensored) is made in selecting informative genes for survival outcome prediction. Applications of our method to published microarray data on epithelial ovarian cancer survival and breast cancer metastasis have identified prognostic genes that predict individual survival and metastatic outcomes with improved power while basing on considerably shorter gene lists.

Keywords

Genetic Algorithm Epithelial Ovarian Cancer Breast Cancer Metastasis Microarray Gene Expression Data Feature Subset Selection 
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 2008

Authors and Affiliations

  • Qihua Tan
    • 1
    • 2
  • Mads Thomassen
    • 1
  • Kirsten M. Jochumsen
    • 1
  • Jing Hua Zhao
    • 3
  • Kaare Christensen
    • 2
  • Torben A. Kruse
    • 1
  1. 1.Dept. of Biochemistry, Pharmacology and GeneticsOdense University HospitalOdense CDenmark
  2. 2.Epidemiology, Institute of Public HealthUniversity of Southern DenmarkOdense CDenmark
  3. 3.MRC Epidemiology UnitInstitute of Metabolic ScienceCambridgeUK

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