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Ai Cell Biology

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Research Frontiers in Graph Neural Networks for Molecular Interaction

Application of graph-based machine learning to model and predict protein-protein interactions and metabolic pathway networks.

Graph Topology Learning in Dynamic Protein Complexes
Message Passing Architectures for Subcellular Compartment Prediction
Equivariant Neural Networks in Biomolecular Symmetry Detection
Heterogeneous Graph Learning Across Multi-Modal Omics Data
Attention Mechanisms for Epistatic Interaction Networks
Graph Pooling Strategies in Single-Cell Spatial Transcriptomics
Neural Graph Kernels for Metabolic Pathway Inference
Temporal Graph Networks in Cell Signaling Cascade Dynamics
Adversarial Robustness in Molecular Interaction Predictions
Graph Generative Models for De Novo Protein Interface Design

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