COSIME: Cooperative multi-view integration and Scalable and Interpretable Model Explainer DOI Open Access

Jerome J. Choi,

Noah Cohen Kalafut,

Tim Gruenloh

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 14, 2025

Single-omics approaches often provide a limited view of complex biological systems, whereas multiomics integration offers more comprehensive understanding by combining diverse data views. However, integrating heterogeneous types and interpreting the intricate relationships between features-both within across different views-remains bottleneck. To address these challenges, we introduce COSIME (Cooperative Multi-view Integration Scalable Interpretable Model Explainer). uses backpropagation Learnable Optimal Transport (LOT) to deep neural networks, enabling learning latent features from multiple views predict disease phenotypes. In addition, incorporates Monte Carlo sampling efficiently estimate Shapley values Shapley-Taylor indices, assessment both feature importance their pairwise interactions-synergistically or antagonistically-in predicting We applied simulated real-world datasets, including single-cell transcriptomics, spatial epigenomics, metabolomics, specifically for Alzheimer's disease-related Our results demonstrate that significantly improves prediction performance while offering enhanced interpretability relationships. For example, identified synergistic interactions microglia astrocyte genes associated with AD are likely be active at edges middle temporal gyrus as indicated locations. Finally, is open-source available general use.

Language: Английский

COSIME: Cooperative multi-view integration and Scalable and Interpretable Model Explainer DOI Open Access

Jerome J. Choi,

Noah Cohen Kalafut,

Tim Gruenloh

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 14, 2025

Single-omics approaches often provide a limited view of complex biological systems, whereas multiomics integration offers more comprehensive understanding by combining diverse data views. However, integrating heterogeneous types and interpreting the intricate relationships between features-both within across different views-remains bottleneck. To address these challenges, we introduce COSIME (Cooperative Multi-view Integration Scalable Interpretable Model Explainer). uses backpropagation Learnable Optimal Transport (LOT) to deep neural networks, enabling learning latent features from multiple views predict disease phenotypes. In addition, incorporates Monte Carlo sampling efficiently estimate Shapley values Shapley-Taylor indices, assessment both feature importance their pairwise interactions-synergistically or antagonistically-in predicting We applied simulated real-world datasets, including single-cell transcriptomics, spatial epigenomics, metabolomics, specifically for Alzheimer's disease-related Our results demonstrate that significantly improves prediction performance while offering enhanced interpretability relationships. For example, identified synergistic interactions microglia astrocyte genes associated with AD are likely be active at edges middle temporal gyrus as indicated locations. Finally, is open-source available general use.

Language: Английский

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