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

Authors
  • Guigui, N.
  • Philippe, C.
  • Gloaguen, A.
  • Karkar, S.
  • Guillemot, V.
  • Löfstedt, T.
  • Frouin, V.
Publication Date
Apr 08, 2019
Source
HAL-INRIA
Keywords
Language
English
License
Unknown
External links

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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