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Inferring Meta-covariates in Classification

  • Keith Harris
  • Lisa McMillan
  • Mark Girolami
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5780)

Abstract

This paper develops an alternative method for gene selection that combines model based clustering and binary classification. By averaging the covariates within the clusters obtained from model based clustering, we define “meta-covariates” and use them to build a probit regression model, thereby selecting clusters of similarly behaving genes, aiding interpretation. This simultaneous learning task is accomplished by an EM algorithm that optimises a single likelihood function which rewards good performance at both classification and clustering. We explore the performance of our methodology on a well known leukaemia dataset and use the Gene Ontology to interpret our results.

Keywords

Gene selection clustering classification EM algorithm Gene Ontology 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Keith Harris
    • 1
  • Lisa McMillan
    • 1
  • Mark Girolami
    • 1
  1. 1.Inference Group, Department of Computing ScienceUniversity of GlasgowUK

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