Explicating a Biological Basis for Chronic Fatigue Syndrome
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 st...
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| Format: | Others |
| Language: | en en |
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2007
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| Online Access: | http://hdl.handle.net/1974/940 |
| Summary: | 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. === Thesis (Master, Computing) -- Queen's University, 2007-12-11 12:15:40.096 |
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