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.
How attentive are graph attention networks? In International Conference on Learning Representations
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Topology-aware large graph representations of polymer chains combined with masked pretraining on unlabeled data reduce prediction error for glass transition temperature by 5.1% compared to repeat-unit baselines.
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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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It's All Connected: Topology-Aware Structural Graph Encoding Improves Performance on Polymer Prediction
Topology-aware large graph representations of polymer chains combined with masked pretraining on unlabeled data reduce prediction error for glass transition temperature by 5.1% compared to repeat-unit baselines.
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