A Graph-Theoretic Approach to Model Genomic Data and Identify Biological Modules Asscociated with Cancer Outcomes
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Studies of the genetic basis of complex disease present statistical and methodological challenges in the discovery of reliable and high-confidence genes that reveal biological phenomena underlying the etiology of disease or gene signatures prognostic of disease outcomes. This dissertation examines the capacity of graph-theoretical methods to model and analyze genomic information and thus facilitate using prior knowledge to create a more discrete and functionally relevant feature space. To assess the statistical and computational value of graph-based algorithms in genomic studies of cancer onset and progression, I apply a random walk graph algorithm in a weighted interaction network. I merge high-throughput co-expression and curated interaction data to search for biological modules associated with key cancer processes and evaluate significant modules in terms of both their predictive value and functional relevance. This approach identifies interactions among genes involved in proliferation, apoptosis, angiogenesis, immune evasion, metastasis, and energy metabolism pathways, and generates hypotheses for future cancer biology studies. Based on the results of this work, I conclude that graph-based approaches are powerful tools for the integration and analysis of complex molecular relationships that reveal significant coordinated activity of genomic features where previous statistical and analytical methods have been limited.