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Network Regularizat...
Network Regularization in Imaging Genetics Improves Prediction Performances and Model Interpretability on Alzheimer’s Disease
- Article/chapterEnglish2019
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IEEE,2019
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Numbers
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LIBRIS-ID:oai:DiVA.org:umu-164661
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https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-164661URI
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https://doi.org/10.1109/ISBI.2019.8759593DOI
Supplementary language notes
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Language:English
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Summary in:English
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Subject category:ref swepub-contenttype
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Subject category:kon swepub-publicationtype
Notes
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Imaging genetics is a growing popular research avenue which aims to find genetic variants associated with quantitative phenotypes that characterize a disease. In this work, we combine structural MRI with genetic data structured by prior knowledge of interactions in a Canonical Correlation Analysis (CCA) model with graph regularization. This results in improved prediction performance and yields a more interpretable model.
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Philippe, C.
(author)
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Gloaguen, A.
(author)
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Karkar, S.
(author)
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Guillemot, V.
(author)
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Löfstedt, TommyUmeå universitet,Radiofysik(Swepub:umu)toklot02
(author)
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Frouin, V.
(author)
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Umeå universitetRadiofysik
(creator_code:org_t)
Related titles
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In:2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019): IEEE, s. 1403-14069781538636411
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