A minimum classification error framework suitable for multicriteria gene selection: discovery of differentially methylated genes in small B-cell lymphomas

  • Authors:

  • Mihail Popescu;Gerald Arthur;Farahnaz Rahmatpanah;Ozy Sjahputera;Huidong Shi;Charles W. Caldwell;James Keller

  • Affiliations:

  • Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, Missouri 65211, USA.;Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, Missouri 65211, USA.;Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, Missouri 65211, USA.;Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, Missouri 65211, USA.;Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, Missouri 65211, USA.;Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, Missouri 65211, USA.;Department of Computer Engineering and Computer Science, University of Missouri, Columbia, Missouri 65211, USA

  • Venue:
  • International Journal of Computational Intelligence in Bioinformatics and Systems Biology
  • Year:
  • 2010

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Abstract

Gene methylation appears to influence cellular activity by silencing the expression of certain genes believed to be responsible for significant biological processes, including neoplastic transformation. In this paper, we present a framework for the identification of differentially methylated genes in several types of small B-cell lymphomas (SBCLs) using DNA differential methylation microarrays. The proposed approach uses the patient classification error to tune various steps of the algorithm such as the data normalisation, the gene ranking and the threshold for gene selection. We present the results obtained by application of the proposed framework on two gene methylation datasets: one with 43 samples and three SBCL types and another with 38 samples and two SBCL subtypes. Some of the identified genes are known to be involved in critical pathways such as apoptosis and proliferation while others function as tumour suppressor genes or oncogenes.