{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101707"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101707","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Optimization, random resampling, and modeling in bioinformatics","abstract":"Quantitative phenotypes regulated by multiple genes are prevalent in nature and many diseases falls into this category. High-throughput sequencing and high-performance computing provides a basis to understand quantitative phenotypes. However, finding a statistical approach correctly model the phenotypes remain a challenging problem. In this work, I present a resampling-based approach to obtain biological functional categories from gene set and apply the approach to analyze lithium-sensitivity of neurological diseases and cancer. 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