Chapter

Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003

Volume 2714 of the series Lecture Notes in Computer Science pp 1073-1080

Date:

Exploring Protein Functional Relationships Using Genomic Information and Data Mining Techniques

  • Jack Y. YangAffiliated withSchool of Electrical and Computer Engineering Purdue University
  • , Mary Qu YangAffiliated withSchool of Electrical and Computer Engineering Purdue University
  • , Okan K. ErsoyAffiliated withSchool of Electrical and Computer Engineering Purdue University

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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.