Network Regularization In Imaging Genetics Improves Prediction Performances And Model Interpretability On Alzheimer'S Disease

N. Guigui,Cathy Philippe, A. Gloaguen,Slim Karkar,Vincent Guillemot,Tommy Löfstedt,Vincent Frouin

2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019)（2019）

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摘要

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.

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关键词

Imaging genetics, Networks, Structured constraints, Generalized Canonical Correlation Analysis

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