Gene Expression Analysis of Leukemia Samples Using Visual Interpretation of Small Ensembles: A Case Study

  • Gregor Stiglic
  • Nawaz Khan
  • Mateja Verlic
  • Peter Kokol
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4774)

Abstract

Many advanced machine learning and statistical methods have recently been employed in classification of gene expression measurements. Although many of these methods can achieve high accuracy, they generally lack comprehensibility of the classification process. In this paper a new method for interpretation of small ensembles of classifiers is used on gene expression data from real-world dataset. It was shown that interactive interpretation systems that were developed for classical machine learning problems also give a great range of possibilities for the scientists in the bioinformatics field. Therefore we chose a gene expression dataset discriminating three types of Leukemia as a testbed for the proposed Visual Interpretation of Small Ensembles (VISE) tool. Our results show that using the accuracy of ensembles and adding comprehensibility gains not only accurate but also results that can possibly represent new knowledge on specific gene functions.

Keywords

gene expression analysis machine learning decision trees 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Gregor Stiglic
    • 1
  • Nawaz Khan
    • 2
  • Mateja Verlic
    • 1
  • Peter Kokol
    • 1
  1. 1.University of Maribor, FERI, Smetanova 17, 2000 MariborSlovenia
  2. 2.School of Computing Science, Middlesex University, The Burrough, Hendon, London NW4 4BTUK

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