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dc.contributor.authorTang, Thomas Cheuk Kaien 14:23:21 (GMT) 14:23:21 (GMT)
dc.description.abstractThis thesis investigates the correlations between short protein peptide sequences and local tertiary structures. In particular, it introduces a novel algorithm for partitioning short protein segments into clusters of local sequence-structure motifs, and demonstrates that these motif clusters contain useful structural information via two applications to structural prediction. The first application utilizes motif clusters to predict local protein tertiary structures. A novel dynamic programming algorithm that performs comparably with some of the best existing algorithms is described. The second application exploits the capability of motif clusters in recognizing regular secondary structures to improve the performance of secondary structure prediction based on Support Vector Machines. Empirical results show significant improvement in overall prediction accuracy with no performance degradation in any specific aspect being measured. The encouraging results obtained illustrate the great potential of using local sequence-structure motifs to tackle protein structure predictions and possibly other important problems in computational biology.en
dc.format.extent1460273 bytes
dc.publisherUniversity of Waterlooen
dc.rightsCopyright: 2004, Tang, Thomas Cheuk Kai. All rights reserved.en
dc.subjectComputer Scienceen
dc.subjectData miningen
dc.subjectSequential and Structural Motif Discoveryen
dc.subjectSecondary Structure Predictionen
dc.subjectLocal Tertiary Structure Predictionen
dc.titleDiscovering Protein Sequence-Structure Motifs and Two Applications to Structural Predictionen
dc.typeMaster Thesisen
dc.pendingfalseen of Computer Scienceen
uws-etd.degreeMaster of Mathematicsen

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