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Ai Flux Balance Analysis

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Ai Flux Balance Analysis

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Research Frontiers in Graph Neural Networks for Pathway Analysis

Implementing graph-based deep learning to model complex metabolic pathway interactions and predict system-wide flux responses.

Message Passing Through Metabolic Flux Landscapes
Graph Equivariance in Dynamic Pathway Rewiring
Neural Bottleneck Detection in Metabolic Networks
Heterogeneous Graph Learning for Multi-Omics Integration
Flux Prediction via Attention-Based Pathway Decomposition
Graph Contrastive Learning in Metabolic Phenotypes
Temporal Graph Networks for Flux State Transitions
Scalable GNN Inference on Genome-Scale Networks
Explainable Node Embeddings for Enzyme Function Prediction
Graph Pooling Strategies for Pathway Module Discovery

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