.png){kind=icon}
返回
KG-MTL: Knowledge Graph Enhanced Multi-Task Learning for Molecular Interaction
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2022.3188154.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Molecular interaction prediction is essential in various applications including drug discovery and material science. The problem becomes quite challenging when the interaction is represented by unmapped relationships in molecular networks, namely molecular interaction, because it easily suffers from (i) insufficient labeled data with many false-positive samples, and (ii) ignoring a large number of biological entities with rich information in the knowledge graph. Most of the existing methods cannot properly exploit the information of knowledge graph and molecule graph simultaneously. In this paper, we propose a large-scale Knowledge Graph enhanced Multi-Task Learning model, namely KG-MTL, which extracts the features from both knowledge graph and molecular graph in a synergistic way. Moreover, we design an effective Shared Unit that helps the model to jointly preserve the semantic relations of drug entity and the neighbor structures of the compound in both knowledge graph and molecular graph. Extensive experiments on four real-world datasets demonstrate that our proposed KG-MTL outperforms the state-of-the-art methods on two representative molecular interaction prediction tasks: drug-target interaction prediction and compound-protein interaction prediction. The source code of KG-MTL is available at https://github.com/xzenglab/KG-MTL.
Keyword:
Drugs
Feature extraction
Task analysis
Diffusion tensor imaging
Knowledge engineering
Semantics
Multitasking
Machine learning
knowledge graph
multi-task learning
drug discovery
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
引用论文
A Computational Framework for Genome-wide Characterization of the Human Disease Landscape.png)
CELL SYSTEMS
OAAI
CELL SYSTEMSIF7.7
OAAI分享收藏
Deciphering the signaling network of breast cancer improves drug sensitivity prediction破译乳腺癌的信号网络提高药物敏感性预测CELL SYSTEMSOAAI
CELL SYSTEMSIF7.7
OAAI分享收藏DrugBank 5.0: a major update to the DrugBank database for 2018DrugBank 5.0: 2018 DrugBank数据库的重大更新NUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAI分享收藏
查看更多内容