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

N. Guigui 1, 2 C. Philippe 1 A. Gloaguen 1, 2 S. Karkar 1 V. Guillemot 3 T. Löfstedt 4 V. Frouin 1
1 UNATI - Unité Analyse et Traitement de l'Information
2 CentraleSupélec
3 C3BI - Centre de Bioinformatique, Biostatistique et Biologie Intégrative
4 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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Conference papers
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N. 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. ⟨cea-02016625⟩

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