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Deep Multiview Learning to Identify Population Structure with Multimodal Imaging
Yixue Feng1, Kefei Liu2, Mansu Kim2
1School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, USA.
This study introduces a deep multiview learning framework using deep generalized canonical correlation analysis (DGCCA) to uncover population structure in Alzheimer's disease cohorts from multimodal imaging data.
Area of Science:
- Neuroimaging
- Computational Biology
- Machine Learning
Background:
- Identifying population structure is crucial for understanding disease heterogeneity.
- Multimodal imaging data offers rich information but poses analytical challenges.
- Existing methods may not fully leverage the complex relationships within multimodal data.
Purpose of the Study:
- To develop an effective deep multiview learning framework for population structure identification.
- To utilize multimodal imaging data for enhanced insights into Alzheimer's disease cohorts.
- To improve upon conventional generalized canonical correlation analysis (GCCA) methods.
Main Methods:
- Proposed a deep generalized canonical correlation analysis (DGCCA) framework.
- Learned a shared latent representation from non-linearly mapped, maximally correlated components.
- Applied cluster analysis to the DGCCA-derived feature set for population structure identification.
Main Results:
- DGCCA captured significantly more variance compared to linear GCCA.
- Identified a promising population structure within an Alzheimer's disease cohort.
- DGCCA-based population structure demonstrated a stronger genetic basis than competing methods.
Conclusions:
- Deep multiview learning with DGCCA is effective for population structure identification using multimodal imaging.
- The learned shared representation enhances genetic association analyses in disease cohorts.
- This framework offers a powerful tool for dissecting disease heterogeneity.
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