Network Regularization in Imaging Genetics Improves Prediction Performances and Model Interpretability on Alzheimer's Disease

N. Guigui, C. Philippe, A. Gloaguen, S. Karkar, V. Guillemot, T. Lofstedt, V. Frouin
2019 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)  
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.
doi:10.1109/isbi.2019.8759593 dblp:conf/isbi/GuiguiPGKGLF19 fatcat:f26iopvsdfhfvm4zrb3m466iue