Network Regularization in Imaging Genetics Improves Prediction Performances and Model Interpretability on Alzheimer’s Disease
- Resource Type
- Conference
- Authors
- Guigui, N.; Philippe, C.; Gloaguen, A.; Karkar, S.; Guillemot, V.; Lofstedt, T.; Frouin, V.
- Source
- 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Biomedical Imaging (ISBI 2019), 2019 IEEE 16th International Symposium on. :1403-1406 Apr, 2019
- Subject
- Bioengineering
Imaging
Diseases
Brain modeling
Data models
Bioinformatics
Genomics
Imaging genetics
Networks
Structured constraints
Generalized Canonical Correlation Analysis
- Language
- ISSN
- 1945-8452
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.