Explicating a Biological Basis for Chronic Fatigue Syndrome
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Authors
Abou-Gouda, Samar A.
Date
2007-12-18T19:15:20Z
Type
thesis
Language
eng
Keyword
Computer Science , Data Mining
Alternative Title
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
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This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.