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Ai Transcriptomics

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Research Frontiers in Gene Regulatory Network Inference Using Graph Neural Networks

Employs graph neural networks to reconstruct gene regulatory networks from transcriptomic data, identifying causal relationships between transcription factors and target genes.

Graph Neural Networks for Temporal Gene Regulation

Attention Mechanisms in Multi-Omics Network Inference

Message Passing Architectures Across Cell State Transitions

Heterogeneous Graph Learning in Regulatory Landscapes

Causal Discovery at the Gene Regulatory Network Edge

Graph Latent Space Representations of Transcriptional Memory

Sparse Graph Neural Networks for Rare Gene Interactions

Adversarial Robustness in Inferred Regulatory Networks

Knowledge-Guided Graph Learning for Disease Perturbations

Topological Complexity in Emergent Regulatory Motifs

All AI Transcriptomics PhD categories