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A multi-level nearest-neighbour algorithm for predicting protein secondary structure

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A multi-level nearest-neighbour algorithm for predicting protein secondary structure

Lazar, Iustin (1998) A multi-level nearest-neighbour algorithm for predicting protein secondary structure. Masters thesis, Concordia University.

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Abstract

A thesis on machine learning and prediction of protein secondary structure. We develop a variation of the nearest-neighbour algorithm that adopts a multi-level strategy together with a variable window size. The algorithm is applied to the problem of predicting the secondary structure of a protein given its primary structure: that is, given a sequence of amino-acids, output a sequence of secondary structures (helix, sheet, or coil). A new training set is developed that is orthogonal, and covers the known classes of proteins. Overall accuracy is 65.0%, with 68.7% accuracy for helices, 66.3% accuracy for sheets, and 61.4% for coils. This compares well with existing methods, in that the best results for a single nearest-neighbour classifier is 65.1% by Salzberg and Cost in 1992. Our accuracy rate for sheets is better than known methods, but our accuracy rate for coils is much lower than existing methods.

Divisions:Concordia University > Faculty of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Lazar, Iustin
Pagination:viii, 120 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:Theses (M.Comp.Sc.)
Program:Computer Science and Software Engineering
Date:1998
Thesis Supervisor(s):Butler, Gregory
ID Code:507
Deposited By:Concordia University Libraries
Deposited On:27 Aug 2009 13:12
Last Modified:08 Dec 2010 10:15
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