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

Nicolas Guigui 1, 2 C. Philippe 1 A. Gloaguen 1, 3 S. Karkar 1 V. Guillemot 4 T. Löfstedt 5 V. Frouin 1
1 UNATI - Unité Analyse et Traitement de l'Information
2 CentraleSupélec
3 L2S - Laboratoire des signaux et systèmes
4 C3BI - Centre de Bioinformatique, Biostatistique et Biologie Intégrative
5 Department of Radiation Sciences, Umea University, Umea, Sweden
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.
Keywords : Index Terms-Imaging-Genetics Networks Structured constraints Generalized Canonical Correlation Analysis
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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
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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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