GLACIER is a single-stage transformer model treating MS/MS fragmentation as subgraph detection on molecular graphs, reporting 70.0% Top-1 accuracy on MassSpecGym and 8x speedup over prior two-stage methods.
Neural graph matching improves retrieval aug- mented generation in molecular machine learning
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A comprehensive survey of graph-based frameworks for higher-order networks, covering foundational concepts, extensions, and newly introduced formalisms with emphasis on structural principles and applications.
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GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem
GLACIER is a single-stage transformer model treating MS/MS fragmentation as subgraph detection on molecular graphs, reporting 70.0% Top-1 accuracy on MassSpecGym and 8x speedup over prior two-stage methods.
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Representing Higher-Order Networks: A Survey of Graph-Based Frameworks
A comprehensive survey of graph-based frameworks for higher-order networks, covering foundational concepts, extensions, and newly introduced formalisms with emphasis on structural principles and applications.