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|Title:||Network-Based Prioritization of Disease Genes, Animal Models, and Drug Targets|
|Advisors:||Troyanskaya, Olga G|
|Contributors:||Computer Science Department|
|Publisher:||Princeton, NJ : Princeton University|
|Abstract:||In living organisms, biomolecules interact in complex molecular networks that underlie cell function and whose dysregulation leads to disease. These networks thus provide a key lens for understanding the molecular basis of human disease as well as treatment development. In this thesis, I develop three network-based computational approaches for human disease research and gaining insight into the mode of action of drugs. At their core, these methods employ functional interaction networks, which provide a genome-wide view of biochemical and pathway-level interactions and summarize essential functional information derived from diverse and heterogeneous functional genomics experiments. First, I propose a network-based method that can detect critical genes and pathways targeted by a drug treatment from gene expression data even in the absence of large-scale expression differences. This approach enables the analysis of low-dose drug screens, ranking potential targets and drug-perturbed biological processes with higher accuracy than prior network-based methods or gene-expression data alone. Furthermore, I present a method that by inferring and comparing genome-wide profiles for human diseases and animal model phenotypes identifies analogous disease models with high accuracy and more robustly than prior methods relying on shared gene content. This method allows to aggregate the wealth of existing model organism knowledge across multiple species and to identify related phenotypes and novel homologous genes of human diseases for experimental follow-up. Lastly, by constructing a joint tissue-specific classifier for human disease genes, we can significantly improve the prediction of associated genes for rare human diseases. This neural network-based approach makes use of a functional network embedding leveraging tissue-specific expression data and model organism phenotype information in a multi-label classification setting. Overall, the methods I developed provide data-driven, molecular-level solutions to major biological challenges relevant to human health.|
|Alternate format:||The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: catalog.princeton.edu|
|Type of Material:||Academic dissertations (Ph.D.)|
|Appears in Collections:||Computer Science|
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