Chapter title |
A novel structure-aware sparse learning algorithm for brain imaging genetics.
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---|---|
Chapter number | 42 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014
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Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, January 2014
|
DOI | 10.1007/978-3-319-10443-0_42 |
Pubmed ID | |
Book ISBNs |
978-3-31-910442-3, 978-3-31-910443-0
|
Authors |
Lei Du, Yan Jingwen, Sungeun Kim, Shannon L Risacher, Heng Huang, Mark Inlow, Jason H Moore, Andrew J Saykin, Shen Li, Du, Lei, Yan, Jingwen, Kim, Sungeun, Risacher, Shannon L., Huang, Heng, Inlow, Mark, Moore, Jason H., Saykin, Andrew J., Shen, Li, Jingwen Yan, Shannon L. Risacher, Jason H. Moore, Andrew J. Saykin, Li Shen |
Abstract |
Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings. |
X Demographics
Geographical breakdown
Country | Count | As % |
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India | 1 | 33% |
Unknown | 2 | 67% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 2% |
Germany | 1 | 2% |
Unknown | 50 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 12 | 23% |
Researcher | 10 | 19% |
Other | 7 | 13% |
Student > Master | 6 | 12% |
Professor | 3 | 6% |
Other | 6 | 12% |
Unknown | 8 | 15% |
Readers by discipline | Count | As % |
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Neuroscience | 11 | 21% |
Computer Science | 6 | 12% |
Engineering | 6 | 12% |
Medicine and Dentistry | 6 | 12% |
Biochemistry, Genetics and Molecular Biology | 3 | 6% |
Other | 7 | 13% |
Unknown | 13 | 25% |