Background: Alzheimer's disease (AD) is an irreversible primary brain disease with insidious onset. The rise of imaging genetics research has led numerous researchers to examine the complex association between genes and brain phenotypes from the perspective of computational biology.
Methods: Given that most previous studies have assumed that imaging data and genetic data are linearly related and are therefore unable to explore their nonlinear relationship, our study applied a joint depth semi-supervised nonnegative matrix decomposition (JDSNMF) algorithm to solve this problem. The JDSNMF algorithm jointly decomposed multimodal imaging genetics data into both a standard basis matrix and multiple feature matrices. During the decomposition process, the coefficient matrix A multilayer nonlinear transformation was performed using a neural network to capture nonlinear features.
Results: The results using a real dataset demonstrated that the algorithm can fully exploit the association between strongly correlated image genetics data and effectively detect biomarkers of AD. Our results might provide a reference for identifying biologically significant imaging genetic correlations, and help to elucidate disease-related mechanisms.
Conclusions: The diagnostic model constructed by the top features of the three modality data sets mined by the algorithm has high accuracy, and these features are expected to become new therapeutic targets for AD.
Keywords: Alzheimer's disease; biomarker; image genetic; joint depth semi-supervised nonnegative matrix decomposition; machine learning.
© 2024 The Author(s). Published by IMR Press.