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Structured Sparse Methods for Imaging Genetics

Abstract Imaging genetics is an emerging and promising technique that investigates how genetic variations affect brain development, structure, and function. By exploiting disorder-related neuroimaging phenotypes, this class of studies provides a novel direction to reveal and understand the complex genetic mechanisms. Oftentimes, imaging genetics studies are challenging due to the relatively small number of subjects but extremely high-dimensionality of both imaging data and genomic data. In this dissertation, I carry on my research on imaging genetics with particular focuses on two tasks---building predictive models between neuroimaging data and genomic data, and identifying disorder-related genetic risk factors through image-based biomarkers. To thi... (more)
Created Date 2017
Contributor Yang, Tao (Author) / Ye, Jieping (Advisor) / Xue, Guoliang (Advisor) / He, Jingrui (Committee member) / Li, Baoxin (Committee member) / Li, Jing (Committee member) / Arizona State University (Publisher)
Subject Computer science / Imaging Genetics / Machine Learning / Optimization / Sparse Models / Structured Sparse Methods
Type Doctoral Dissertation
Extent 134 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Computer Science 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis