Search Results for author:
Found 5 papers, 3 papers with code
S$^2$DN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph Completion
These results demonstrate the effectiveness of S$^2$DN in preserving semantic consistency and enhancing the robustness of filtering out unreliable interactions in contaminated KGs.
MaskMol: Knowledge-guided Molecular Image Pre-Training Framework for Activity Cliffs
Activity cliffs, which refer to pairs of molecules that are structurally similar but show significant differences in their potency, can lead to model representation collapse and make the model challenging to distinguish them.
KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion
Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web.
Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction
To address this limitation, we propose BioKDN (Biomedical Knowledge Graph Denoising Network) for robust molecular interaction prediction.
Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs).
