Prediction of Drug–Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding
Nowadays, drug–target interactions (DTIs) prediction is a fundamental part of drug repositioning. However, on the one hand, drug–target interactions prediction models usually consider drugs or targets information, which ignore prior knowledge between drugs and targets. On the other hand, models inco...
Main Authors: | Jiaxin Li, Xixin Yang, Yuanlin Guan, Zhenkuan Pan |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-08-01
|
Series: | Molecules |
Subjects: | |
Online Access: | https://www.mdpi.com/1420-3049/27/16/5131 |
Similar Items
-
Probabilistic Coarsening for Knowledge Graph Embeddings
by: Marcin Pietrasik, et al.
Published: (2023-03-01) -
IntME: Combined Improving Feature Interactions and Matrix Multiplication for Convolution-Based Knowledge Graph Embedding
by: Haonan Zhang, et al.
Published: (2023-08-01) -
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) -
Embedding Uncertain Temporal Knowledge Graphs
by: Tongxin Li, et al.
Published: (2023-02-01) -
Language Model Guided Knowledge Graph Embeddings
by: Mirza Mohtashim Alam, et al.
Published: (2022-01-01)