Support Vector Based T-Score for Gene Ranking

  • Piyushkumar A. Mundra
  • Jagath C. Rajapakse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5265)


T-score between classes and gene expressions is widely used for gene ranking in microarray gene expression data analysis. We propose to use only support vector points for computation of t-scores for gene ranking. The proposed method uses backward elimination of features, similar to Support Vector Machine Recursive Feature Elimination (SVM-RFE) formulation, but achieves better results than SVM-RFE and t-score based feature selection on three benchmark cancer datasets.


Support Vector Machine Support Vector Gene Ranking Gene Subset Cancer Dataset 
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

  • Piyushkumar A. Mundra
    • 1
  • Jagath C. Rajapakse
    • 1
    • 2
    • 3
  1. 1.Bioinformatics Research Center, School of Computer EngineeringNanyang Technological UniversitySingapore
  2. 2.Singapore-MIT AllianceSingapore
  3. 3.Department of Biological EngineeringMassachusetts Institute of TechnologyUSA

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