Search: onr:"swepub:oai:DiVA.org:umu-164661" >
Network Regularizat...
Network Regularization in Imaging Genetics Improves Prediction Performances and Model Interpretability on Alzheimer’s Disease
-
Guigui, N. (author)
-
Philippe, C. (author)
-
Gloaguen, A. (author)
-
show more...
-
Karkar, S. (author)
-
Guillemot, V. (author)
-
- Löfstedt, Tommy (author)
- Umeå universitet,Radiofysik
-
Frouin, V. (author)
-
show less...
-
(creator_code:org_t)
- IEEE, 2019
- 2019
- English.
-
In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). - : IEEE. - 9781538636411 ; , s. 1403-1406
- Related links:
-
https://urn.kb.se/re...
-
show more...
-
https://doi.org/10.1...
-
show less...
Abstract
Subject headings
Close
- 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.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Keyword
- Imaging genetics
- Networks
- Structured constraints
- Generalized Canonical Correlation Analysis
Publication and Content Type
- ref (subject category)
- kon (subject category)
Find in a library
To the university's database