Mixed Models with Multiple Instance Learning
International Conference on Artificial Intelligence and Statistics(2023)
摘要
Predicting patient features from single-cell data can help identify cellular
states implicated in health and disease. Linear models and average cell type
expressions are typically favored for this task for their efficiency and
robustness, but they overlook the rich cell heterogeneity inherent in
single-cell data. To address this gap, we introduce MixMIL, a framework
integrating Generalized Linear Mixed Models (GLMM) and Multiple Instance
Learning (MIL), upholding the advantages of linear models while modeling cell
state heterogeneity. By leveraging predefined cell embeddings, MixMIL enhances
computational efficiency and aligns with recent advancements in single-cell
representation learning. Our empirical results reveal that MixMIL outperforms
existing MIL models in single-cell datasets, uncovering new associations and
elucidating biological mechanisms across different domains.
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