Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics

  • Jan Struyf
  • Sašo Džeroski
  • Hendrik Blockeel
  • Amanda Clare
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3808)

Abstract

This paper investigates how predictive clustering trees can be used to predict gene function in the genome of the yeast Saccharomyces cerevisiae. We consider the MIPS FunCat classification scheme, in which each gene is annotated with one or more classes selected from a given functional class hierarchy. This setting presents two important challenges to machine learning: (1) each instance is labeled with a set of classes instead of just one class, and (2) the classes are structured in a hierarchy; ideally the learning algorithm should also take this hierarchical information into account. Predictive clustering trees generalize decision trees and can be applied to a wide range of prediction tasks by plugging in a suitable distance metric. We define an appropriate distance metric for hierarchical multi-classification and present experiments evaluating this approach on a number of data sets that are available for yeast.

Keywords

Average Precision Yeast Gene Prediction Task Multitask Learning Hierarchical Information 
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 2005

Authors and Affiliations

  • Jan Struyf
    • 1
  • Sašo Džeroski
    • 2
  • Hendrik Blockeel
    • 1
  • Amanda Clare
    • 3
  1. 1.Dept. of Computer ScienceKatholieke Universiteit LeuvenLeuvenBelgium
  2. 2.Dept. of Knowledge TechnologiesJozef Stefan InstituteLjubljanaSlovenia
  3. 3.Dept. of Computer ScienceThe University of Wales, AberystwythPenglais, Aberystwyth, CeredigionUK

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