| dc.description.abstract |
<p>Cancer is a complex, multifaceted disease that operates through dynamic changes
in the genome. Cancer is best understood through the process that generates it --
random mutations operated on by natural selection -- and several global hallmarks
that describe its broad mechanisms. While many genes, protein interactions, and pathways
have been enumerated as a kind of ``parts'' list for cancer, researchers are attempting
to synthesize broader models for inferring and predicting cancer behavior using high-throughput
data and integrative analyses. </p><p>The focus of this thesis is on the development
of two novel methods that are optimized for the analysis of complex cancer phenotypes.
The first method incorporates ideas from gradient learning with multitask learning
to assess statistical dependencies across multiple related data sets. The second
method integrates multiscale analysis on graphs and manifolds developed in applied
harmonic analysis with sparse factor models, a mainstay of applied statistics. This
method generates multiscale factors that are used for inferring hierarchical associations
within complex biological networks. The primary biological focus is the inference
of gene and pathway dependencies associated with cancer progression and metastatic
disease in prostate cancer. Significant findings include evidence of Skp2 degradation
of the cell-cycle regulator p27, and the upstream deregulation of the TGF-beta pathway,
driving prostate cancer recurrence.</p>
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