AI RESEARCH
Event Embedding of Protein Networks : Compositional Learning of Biological Function
arXiv CS.LG
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ArXi:2604.00911v1 Announce Type: new In this work, we study whether enforcing strict compositional structure in sequence embeddings yields meaningful geometric organization when applied to protein-protein interaction networks. Using Event2Vec, an additive sequence embedding model, we train 64-dimensional representations on random walks from the human STRING interactome, and compare against a DeepWalk baseline based on Word2Vec, trained on the same walks.