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Network regularization in imaging genetics improves prediction performances and model interpretability on Alzheimers's disease

Abstract : 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 inter-pretable model.
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https://hal-cea.archives-ouvertes.fr/cea-02016625
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Submitted on : Tuesday, February 12, 2019 - 5:55:55 PM
Last modification on : Wednesday, September 16, 2020 - 4:49:09 PM
Long-term archiving on: : Monday, May 13, 2019 - 4:20:11 PM

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Nicolas Guigui, C. Philippe, A. Gloaguen, S. Karkar, V. Guillemot, et al.. Network regularization in imaging genetics improves prediction performances and model interpretability on Alzheimers's disease. ISBI 2019 - Proceedings of the IEEE International Symposium on Biomedical Imaging, Apr 2019, Venice, Italy. ⟨10.1109/ISBI.2019.8759593⟩. ⟨cea-02016625⟩

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