Simulated MeSH Hierarchical Dataset with Poincaré Hyperbolic Distances

This dataset simulates one million hierarchical relationships between biomedical concepts inspired by the MeSH vocabulary, capturing valid parent-child and unrelated pairs. Each entry contains unique IDs for parent and child concepts, their hierarchical tree numbers, a hyperbolic distance score representing their proximity in a Poincaré embedding space, and a binary label indicating whether the child is a true hierarchical descendant. Valid child nodes have tree numbers extending their parent’s and low hyperbolic distances (0.01–0.4), while invalid pairs have unrelated tree numbers and larger distances (0.8–2.0). This dataset supports research on hierarchical representation learning and classification in biomedical ontologies. Finally, the dataset is part of the larger project regarding oncological solving via computational biology in AURORA.