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/1974/940

Title: Explicating a Biological Basis for Chronic Fatigue Syndrome
Authors: Abou-Gouda, Samar A.

Files in This Item:

File Description SizeFormat
AbouGouda_Samar_A_200712_MSc.pdf821.36 kBAdobe PDFView/Open
Keywords: Computer Science
Data Mining
Issue Date: 2007
Series/Report no.: Canadian theses
Abstract: In the absence of clinical markers for Chronic Fatigue Syndrome (CFS), research to find a biological basis for it is still open. Many data-mining techniques have been widely employed to analyze biomedical data describing different aspects of CFS. However, the inconsistency of the results of these studies reflect the uncertainty in regards to the real basis of this disease. In this thesis, we show that CFS has a biological basis that is detectable in gene expression data better than blood profile and Single Nucleotide Polymorphism (SNP) data. Using random forests, the analysis of gene expression data achieves a prediction accuracy of approximately 89%. We also identify sets of differentially expressed candidate genes that might contribute to CFS. We show that the integration of data spanning multiple levels of the biological scale might reveal further insights into the understanding of CFS. Using integrated data, we achieve a prediction accuracy of approximately 91%. We find that Singular Value Decomposition (SVD) is a useful technique to visualize the performance of random forests.
Description: Thesis (Master, Computing) -- Queen's University, 2007-12-11 12:15:40.096
URI: http://hdl.handle.net/1974/940
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