Journal
JOURNAL OF NEUROSCIENCE METHODS
Volume 237, Issue -, Pages 69-78Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jneumeth.2014.09.001
Keywords
Imaging genetics; Sparse modeling; Correspondence analysis; Integration; Classification
Categories
Funding
- NIH grants [NIBIB 2R01EB000840, COBRE 5P20RR021938/P20GM103472, R01GM109068, R01MH104680]
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The development of advanced medical imaging technologies and high-throughput genomic measurements has enhanced our ability to understand their interplay as well as their relationship with human behavior by integrating these two types of datasets. However, the high dimensionality and heterogeneity of these datasets presents a challenge to conventional statistical methods; there is a high demand for the development of both correlative and integrative analysis approaches. Here, we review our recent work on developing sparse representation based approaches to address this challenge. We show how sparse models are applied to the correlation and integration of imaging and genetic data for biomarker identification. We present examples on how these approaches are used for the detection of risk genes and classification of complex diseases such as schizophrenia. Finally, we discuss future directions on the integration of multiple imaging and genomic datasets including their interactions such,as epistasis. (C) 2014 Elsevier B.V. All rights reserved.
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