Exploring Protein Functional Relationships Using Genomic Information and Data Mining Techniques

  • Jack Y. Yang
  • Mary Qu Yang
  • Okan K. Ersoy
Conference paper

DOI: 10.1007/3-540-44989-2_128

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2714)
Cite this paper as:
Yang J.Y., Yang M.Q., Ersoy O.K. (2003) Exploring Protein Functional Relationships Using Genomic Information and Data Mining Techniques. In: Kaynak O., Alpaydin E., Oja E., Xu L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg

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.

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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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