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Ai Bioinstrumentation

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Ai Bioinstrumentation

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Research Frontiers in Graph Neural Networks for Metabolic Pathway Analysis

Application of GNNs to model and predict metabolic dynamics from multi-omics biosensor data integrated with instrumental measurements.

Latent Geometry of Metabolic Network Topology
Message Passing Through Enzymatic Constraint Spaces
Graph Attention for Cofactor-Mediated Pathway Prediction
Temporal Dynamics in Metabolic Flux Networks
Heterogeneous Node Embedding for Multi-Organism Pathways
Equivariant Learning on Biochemical Reaction Graphs
Metabolite Diffusion Models via Graph Neural Dynamics
Substrate Specificity From Graph Spectral Features
Cross-Scale Integration in Metabolic Architecture
Emergent Pathway Modules Through Graph Contrastive Learning

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