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Network Regularization in Imaging Genetics Improves Prediction Performances and Model Interpretability on Alzheimer’s Disease
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2019 (English)In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), IEEE, 2019, p. 1403-1406Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2019. p. 1403-1406
Series
IEEE International Symposium on Biomedical Imaging, ISSN 1945-7928
Keywords [en]
Imaging genetics, Networks, Structured constraints, Generalized Canonical Correlation Analysis
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:umu:diva-164661DOI: 10.1109/ISBI.2019.8759593ISI: 000485040000295Scopus ID: 2-s2.0-85073889788ISBN: 978-1-5386-3641-1 (electronic)OAI: oai:DiVA.org:umu-164661DiVA, id: diva2:1370264
Conference
ISBI 2019, 2019 IEEE International Symposium on Biomedical Imaging, April 8–11, 2019, Venice, Italy
Available from: 2019-11-14 Created: 2019-11-14 Last updated: 2019-11-14Bibliographically approved

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Löfstedt, Tommy

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