Exploring Protein Functional Relationships Using Genomic Information and Data Mining Techniques
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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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- Exploring Protein Functional Relationships Using Genomic Information and Data Mining Techniques
- Book Title
- Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003
- Book Subtitle
- Joint International Conference ICANN/ICONIP 2003 Istanbul, Turkey, June 26–29, 2003 Proceedings
- pp 1073-1080
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- Series Title
- Lecture Notes in Computer Science
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- Springer Berlin Heidelberg
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- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 4. Bogazici University
- 5. Laboratory of Computer and Information Science, Helsinki University of Technology
- 6. Department of Computer Science and Engineering, The Chinese University of Hong Kong
- Author Affiliations
- 7. School of Electrical and Computer Engineering Purdue University, West Lafayette, IN, 47907-1285, USA
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