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Found 9 papers, 2 papers with code
Graph Neural Aggregation-diffusion with Metastability
Due to the connection between graph diffusion and message passing, diffusion-based models have been widely studied.
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Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory
This paper presents a novel study of the oversmoothing issue in diffusion-based Graph Neural Networks (GNNs).
ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease
ADMarker features a novel three-stage multi-modal federated learning architecture that can accurately detect digital biomarkers in a privacy-preserving manner.
FFF: Fragments-Guided Flexible Fitting for Building Complete Protein Structures
This has inspired us to combine fragment recognition and structure prediction methods to build a complete structure.
Knowledge Graph Completion based on Tensor Decomposition for Disease Gene Prediction
In this paper, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end Knowledge graph completion model for Disease Gene Prediction using interactional tensor decomposition (called KDGene).
A Pre-training Framework for Knowledge Graph Completion
Knowledge graph completion (KGC) is one of the effective methods to identify new facts in knowledge graph.
FFF: Fragment-Guided Flexible Fitting for Building Complete Protein Structures
This has inspired us to combine fragment recognition and structure prediction methods to build a complete structure.
Network medicine framework reveals generic herb-symptom effectiveness of Traditional Chinese Medicine
We validate our framework with real-world hospital patient data by showing that (1) shorter network distance between symptoms of inpatients correlates with higher relative risk (co-occurrence), and (2) herb-symptom network proximity is indicative of patients' symptom recovery rate after herbal treatment.
TCMPR: TCM Prescription recommendation based on subnetwork term mapping and deep learning
In addition, the comprehensive experiments of TCMPR with different hyper parameters (i. e., feature embedding, feature dimension and feature fusion) that demonstrates that our method has high performance on TCM prescription recommendation and potentially promote clinical diagnosis and treatment of TCM precision medicine.
