GNNs with ontology-derived semantic loss create hierarchy-aware box embeddings of a yeast knowledge graph that raise double-knockout growth prediction R² to 0.377 and generalize to triple knockouts while identifying a validated trait association.
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OptimusKG is a labeled property graph unifying biomedical knowledge from structured sources into 190,531 nodes of 10 types and 21.8 million edges of 26 types, with 70% of sampled edges supported by literature evidence via PaperQA3 validation.
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Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction
GNNs with ontology-derived semantic loss create hierarchy-aware box embeddings of a yeast knowledge graph that raise double-knockout growth prediction R² to 0.377 and generalize to triple knockouts while identifying a validated trait association.
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OptimusKG: Unifying biomedical knowledge in a modern multimodal graph
OptimusKG is a labeled property graph unifying biomedical knowledge from structured sources into 190,531 nodes of 10 types and 21.8 million edges of 26 types, with 70% of sampled edges supported by literature evidence via PaperQA3 validation.