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Exploring Protein Functional Relationships Using Genomic Information and Data Mining Techniques

  • Jack Y. Yang
  • Mary Qu Yang
  • Okan K. Ersoy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2714)

Abstract

Anapproach that uses both supervised and unsupervised learning methods for exploring protein functional relationships is reported; we refer to this as Maximum Contrast (MC) tree. The tree is constructed by performing a hierarchical decomposition of the feature space; this step is performed regardless of complex nature of protein functions, i.e. it performs this decomposition even without knowledge of the protein functional class labels. In order to test our algorithm, we have constructed a library of Protein Phylogenetic Profiles for the proteins in the yeast Saccharomyces Cerevisiae with 60 species. Results showed our algorithm compares favorably to other classification algorithms such as the decision tree algorithms C4.5, C5, and to support vector machines.

Keywords

Support Vector Machine Feature Space Leaf Node Class Label Test Instance 
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 2003

Authors and Affiliations

  • Jack Y. Yang
    • 1
  • Mary Qu Yang
    • 1
  • Okan K. Ersoy
    • 1
  1. 1.School of Electrical and Computer Engineering Purdue UniversityWest LafayetteUSA

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