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