Pattern Recognition in Bioinformatics

Volume 4774 of the series Lecture Notes in Computer Science pp 189-197

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

  • Gregor StiglicAffiliated withUniversity of Maribor, FERI, Smetanova 17, 2000 Maribor
  • , Nawaz KhanAffiliated withSchool of Computing Science, Middlesex University, The Burrough, Hendon, London NW4 4BT
  • , Mateja VerlicAffiliated withUniversity of Maribor, FERI, Smetanova 17, 2000 Maribor
  • , Peter KokolAffiliated withUniversity of Maribor, FERI, Smetanova 17, 2000 Maribor


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.


gene expression analysis machine learning decision trees