An organism's phenome is the expression of characteristics from genetic inheritance and interaction with the environment. This includes simple physical appearance and traits, and even complex diseases. In human, the understanding of the relationship of such features with genetic markers gives insights into the mechanisms involved in the expression, and can also help to design targeted therapies and new drugs. In other species, such as plants, correlation of phenotypes with genetic mutations and geoclimatic variables also assists in the understanding of evolutionary global diversity and important characteristics such as flowering time. In this thesis, we propose to use high-order machine learning methods to help in the analysis of phenome through the associations with biological networks and ontologies. We show that, by combining biological networks with functional annotation of genes, we can extract high-order relations to improve the discovery of new candidate associations between genes and phenotypes. We also propose to detect high-order relations among multiple genomics datasets, geoclimatic features, and interactions among genes, to find a feature representation that can be utilized to successfully predict phenotypes. Experiments using the Arabidopsis thaliana species shows that our approach does not only contribute with an accurate predictive tool, but also brings an intuitive alternative for the analysis of correlation among plant accessions, genetic markers, and geoclimatic variables. Finally, we propose a scalable approach to solve challenges inherited from the use of massive biological networks in phenome analysis. Our low-rank method can be used to process massive networks in parallel computing to enable large-scale prior knowledge to be incorporated and improve predictive power.
University of Minnesota Ph.D. dissertation. August 2019. Major: Computer Science. Advisor: Rui Kuang. 1 computer file (PDF); ix, 96 pages.
Learning High-Order Relations for Network-Based Phenome-Genome Association Analysis.
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