Queen's University - Utility Bar

QSpace at Queen's University >
Graduate Theses, Dissertations and Projects >
Queen's Graduate Theses and Dissertations >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1974/1484

Title: The Universal Similarity Metric, Applied to Contact Maps Comparison in A Two-Dimensional Space
Authors: Rahmati, Sara

Files in This Item:

File Description SizeFormat
Rahmati_Sara_200809_MSc.pdf1.38 MBAdobe PDFView/Open
Keywords: bioinformatics
kolmogorov complexity
universal similarity metric
protein contact map
contact map comparison
protein structure prediction
Issue Date: 2008
Series/Report no.: Canadian theses
Abstract: Comparing protein structures based on their contact maps is an important problem in structural proteomics. Building a system for reconstructing protein tertiary structures from their contact maps is one of the motivations for devising novel contact map comparison algorithms. Several methods that address the contact map comparison problem have been designed which are briefly discussed in this thesis. However, they suggest scoring schemes that do not satisfy the two characteristics of “metricity” and “universality”. In this research we investigate the applicability of the Universal Similarity Metric (USM) to the contact map comparison problem. The USM is an information theoretical measure which is based on the concept of Kolmogorov complexity. The ultimate goal of this research is to use the USM in case-based reasoning system to predict protein structures from their predicted contact maps. The fact that the contact maps that will be used in such a system are the ones which are predicted from the protein sequences and are not noise-free, implies that we should investigate the noise-sensitivity of the USM. This is the first attempt to study the noise-tolerance of the USM. In this research, as the first implementation of the USM we converted the two-dimensional data structures (contact maps) to one-dimensional data structures (strings). The results of this implementation motivated us to circumvent the dimension reduction in our second attempt to implement the USM. Our suggested method in this thesis has the advantage of obtaining a measure which is noise tolerant. We assess the effectiveness of this noise tolerance by testing different USM implementation schemes against noise-contaminated versions of distinguished data-sets.
Description: Thesis (Master, Computing) -- Queen's University, 2008-09-27 05:53:31.988
URI: http://hdl.handle.net/1974/1484
Appears in Collections:Queen's Graduate Theses and Dissertations
School of Computing Graduate Theses

Items in QSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


  DSpace Software Copyright © 2002-2008  The DSpace Foundation - TOP