- 10-14王鑫雷 The APVA-TURBO Approach to Question Answering in Knowledge Base
- 05-20李楠 Generalizing Biomedical Relation Classification with Neural Adversarial Domain Adaptation
- 04-23李青青 Deep learning for drug-drug interaction extraction
- 04-15刁宇峰 AAAI2018中的自注意力机制(Self-attention Mechanism)
- 04-15王治政 Label informed Attributed Network Embedding
- 03-24罗凌 自然语言处理中的自注意力机制（Self-attention Mechanism）
接到编辑部通知，博士生桑盛田的论文 "GrEDeL:A Knowledge Graph Embedding Based Method for Drug Discovery from Biomedical Literatures" 被IEEE Access录用，该期刊是影响因子为3.557的SCI期刊。
Drug discovery is the process by which new candidate medications are discovered. Developing a new drug is a lengthy, complex, and expensive process. Here, we propose a biomedical knowledge graph embedding based recurrent neural network method called GrEDeL which discovers potential drugs for diseases by mining published biomedical literature. GrEDeL first build a biomedical knowledge graph by exploiting the relations extracted from biomedical abstracts. Then, the graph data are converted into a low dimensional space by leveraging the knowledge graph embedding methods. After that, a recurrent neural network model is trained by the known drug therapies which are represented by graph embeddings. Finally, it uses the learned model to discover candidate drugs for diseases of interest from biomedical literature. The experimental results show that our method could not only effectively discover new drugs by mining literature, but also could provide the corresponding mechanism of actions for the candidate drugs. It could be a supplementary method for current traditional drug discovery methods.