<html><body><div id="zimbraEditorContainer" style="font-family: garamond,new york,times,serif; font-size: 12pt; color: #000000" class="11"><div></div><div data-marker="__QUOTED_TEXT__"><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">Vicky Yao will present her FPO "Integrative network-based approaches to analyze genomics data" <span style="font-size: 12pt;" data-mce-style="font-size: 12pt;">on Friday, 8/24/2018 at 1:00pm in 200 CIL.</span></p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">The members of her committee are as follows: Readers: Mona Singh and Barbara Engelhardt; Examiners: Ryan Adams, Coleen Murphy (MOL), and Olga Troyanskaya (adviser).</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">A copy of her thesis is available upon request.</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">Everyone is invited to attend her talk. The talk abstract follows below.<br></p><div></div><div data-marker="__QUOTED_TEXT__">The generation of diverse genome-scale data across organisms and experimental</div><div data-marker="__QUOTED_TEXT__">conditions is becoming increasingly commonplace, creating unprecedented opportunities</div><div data-marker="__QUOTED_TEXT__">for understanding the molecular underpinnings of human disease. However, these</div><div data-marker="__QUOTED_TEXT__">large data are often noisy, highly heterogeneous, and lack the resolution required to</div><div data-marker="__QUOTED_TEXT__">study key aspects of metazoan complexity, such as tissue and cell-type specificity. Furthermore,</div><div data-marker="__QUOTED_TEXT__">targeted data collection and experimental verification is often infeasible in</div><div data-marker="__QUOTED_TEXT__">humans, underscoring the need for methods that can integrate -omics data, computational</div><div data-marker="__QUOTED_TEXT__">predictions, and biological knowledge across organisms.</div><div data-marker="__QUOTED_TEXT__">In this dissertation, I describe several novel, integrative computational approaches</div><div data-marker="__QUOTED_TEXT__">to address these challenges. First, I will describe a statistical and machine learning</div><div data-marker="__QUOTED_TEXT__">approach that takes advantage of high-quality neuron-specific molecular profiles of</div><div data-marker="__QUOTED_TEXT__">cells that vary in vulnerability to Alzheimer’s disease obtained in mouse to generate</div><div data-marker="__QUOTED_TEXT__">neuron-specific functional networks in human. We then combine these network</div><div data-marker="__QUOTED_TEXT__">models with human quantitative genetics data to prioritize likely Alzheimer’s disease</div><div data-marker="__QUOTED_TEXT__">candidates. Next, I present an in-depth analysis of all major adult C. elegans tissues</div><div data-marker="__QUOTED_TEXT__">and genome-wide expression predictions across 76 tissues and cell types. The tissue</div><div data-marker="__QUOTED_TEXT__">expression prediction method is one of the building blocks of diseaseQUEST, an integrative</div><div data-marker="__QUOTED_TEXT__">computational-experimental framework that combines human quantitative</div><div data-marker="__QUOTED_TEXT__">genetics with in silico functional network representations of model organism biology</div><div data-marker="__QUOTED_TEXT__">to systematically identify disease gene candidates. This framework leverages a</div><div data-marker="__QUOTED_TEXT__">novel semi-supervised Bayesian network integration approach to predict tissue- and</div><div data-marker="__QUOTED_TEXT__">cell-type specific functional relationships between genes in model organisms. We use</div><div data-marker="__QUOTED_TEXT__">diseaseQUEST to construct 203 tissue- and cell-type specific functional networks and</div><div data-marker="__QUOTED_TEXT__">predict candidate genes for 25 different human diseases and traits using C. elegans as</div><div data-marker="__QUOTED_TEXT__">a model system, with a particular focus on Parkinson’s disease. Finally, I will present a</div><div data-marker="__QUOTED_TEXT__">network-based approach that systematically identifies differential isoforminteractions.</div><div data-marker="__QUOTED_TEXT__">We apply this approach to the study of tissue and environment dynamics in Alzheimer’s</div><div data-marker="__QUOTED_TEXT__">disease. Together, these approaches provide a framework for addressing the challenges</div><div data-marker="__QUOTED_TEXT__">of data heterogeneity, noise, and biological resolution in human molecular data to</div><div data-marker="__QUOTED_TEXT__">better understand the etiology of human disease.</div><div data-marker="__QUOTED_TEXT__"><br></div></div></div></body></html>