Understanding the performance of knowledge graph embeddings in drug discovery
Knowledge Graphs (KG) and associated Knowledge Graph Embedding (KGE) models have recently begun to be explored in the context of drug discovery and have the potential to assist in key challenges such as target identification. In the drug discovery domain, KGs can be employed as part of a process whi...
Main Authors: | Stephen Bonner, Ian P. Barrett, Cheng Ye, Rowan Swiers, Ola Engkvist, Charles Tapley Hoyt, William L. Hamilton |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2022-12-01
|
Series: | Artificial Intelligence in the Life Sciences |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2667318522000071 |
Similar Items
-
Probabilistic Coarsening for Knowledge Graph Embeddings
by: Marcin Pietrasik, et al.
Published: (2023-03-01) -
Embedding Uncertain Temporal Knowledge Graphs
by: Tongxin Li, et al.
Published: (2023-02-01) -
Advances in Knowledge Graph Embedding Based on Graph Neural Networks
by: YAN Zhaoyao, DING Cangfeng, MA Lerong, CAO Lu, YOU Hao
Published: (2023-08-01) -
CIST: Differentiating Concepts and Instances Based on Spatial Transformation for Knowledge Graph Embedding
by: Pengfei Zhang, et al.
Published: (2022-09-01) -
IntME: Combined Improving Feature Interactions and Matrix Multiplication for Convolution-Based Knowledge Graph Embedding
by: Haonan Zhang, et al.
Published: (2023-08-01)