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This paper proposes a methodology for crafting principled frequency-based adversarial attacks, via a dedicated optimization framework, and proves that the attacks emerge as weighted $\ell_2$-projections onto this set, yielding a general and controlled attack generation mechanism.
ProtScape, a multiscale graph-learning framework integrating global protein interactions, cell-type gene expression and protein language models to learn context-specific representations and infer interactomes across more than 200 cell types, substantially outperforms existing approaches in interaction reconstruction.
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