The paper investigates hyperbolic graph representation learning applied to biomedical knowledge graphs for Mendelian-disease differential diagnosis. It shows that hyperbolic embeddings outperform Euclidean baselines on isolated ontology subgraphs while requiring fewer dimensions. In a link-prediction task, hyperbolic models rank candidate diseases for patients, indicating they can leverage hierarchical structure in heterogeneous patient-level graphs.
Functional brain networks exhibit a hierarchical organization across ROI, community, and whole-brain levels, supporting local processing, inter-community coordination, and global integration. Recent studies have demonstrated that brain community-aware modeling is beneficial for both diagnosis and biomarker identification of brain networks.
arXiv:2607. 07077v1 Announce Type: cross Abstract: Functional brain networks exhibit a hierarchical organization across ROI, community, and whole-brain levels, supporting local processing, inter-community coordination, and global integration.
The paper introduces UdonCare, a hierarchy‑pruning method that iteratively partitions patients into latent domains using medical ontologies, aiming to improve domain generalization in clinical prediction tasks. It addresses challenges of missing domain labels and lack of clinical insight by discovering hierarchy‑grounded patient domains. Experiments on MIMIC‑III, MIMIC‑IV, and eICU datasets show UdonCare outperforms eight baseline methods across four prediction tasks with significant domain gaps.
arXiv:2606. 15155v1 Announce Type: new Abstract: Knowledge graphs (KGs) have emerged as a promising solution for integrating and reasoning over complex biomedical and clinical data in healthcare.
arXiv:2606. 05994v1 Announce Type: new Abstract: Medical knowledge graphs (MKGs) infused with clinical knowledge have been increasingly used to model electronic health records (EHRs) to support interpretable predictions in healthcare domain.
arXiv:2608. 06713v1 Announce Type: new Abstract: Biomedical knowledge graphs (KGs) accelerate drug discovery, but standard pipelines assume query molecules already exist as graph entities, leaving unregistered molecules disconnected.
arXiv:2608. 06253v1 Announce Type: new Abstract: Metabolomics knowledge is distributed across heterogeneous resources and remains difficult to translate into predictive representations.
OptimusKG is a multimodal biomedical labeled property graph that integrates structured and semi‑structured resources to preserve detailed, type‑specific metadata across molecular, anatomical, clinical, and environmental domains. The graph contains nearly 191,000 nodes, over 21.8 million edges, and more than 67 million property instances derived from 18 ontologies, with a top‑level schema that enforces node and edge constraints while retaining granular provenance. Validation using the PaperQA3 agent found that 70.0% of sampled edges are supported by literature evidence, and the graph offers a standardized resource for machine learning, knowledge‑grounded retrieval, and hypothesis generation in biomedical research.
arXiv:2606. 15447v1 Announce Type: new Abstract: Electronic health record foundation models typically treat ICD diagnosis codes as flat tokens, overlooking the clinically meaningful hierarchical structure that captures disease families, subcategories, and fine-grained diagnostic detail.
arXiv:2607. 25609v1 Announce Type: cross Abstract: Understanding disease trajectories from longitudinal clinical data remains challenging due to complex temporal dynamics and heterogeneous patient cohorts.
arXiv:2609.06779v1 Announce Type: cross Abstract: Drug repurposing aims to identify new therapeutic uses for existing compounds and, compared with de novo drug discovery, offers a faster and more cos...
The paper introduces the Relational Hypergraph Transformer (RHT), a unified architecture that models relational databases as hypergraphs and learns pentadimensional embeddings (PentE). RHT applies sparse relational attention whose complexity scales with the average relational degree, making it computationally efficient for large, high‑dimensional, and high‑cardinality datasets. Experiments on the Synthea synthetic electronic health record dataset show that RHT produces more semantically coherent embeddings than tabular, relational, and temporal graph baselines, while remaining scalable, and the authors provide an open‑source implementation and plan clinical validation on MIMIC‑IV.