博士生桑盛田论文被IEEE Access录用
新闻来源:IR实验室       发布时间:2018/12/11 16:29:39

  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。

  Abstract:

  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.

 

  摘要:

  药物发现是寻找潜在药物的过程。由于开发一款新药是一个耗时、复杂且昂贵的过程,本文提出了一种基于生物医学知识图谱嵌入的循环神经网络方法-GrEDeL-从已发表的生物医学文献中挖掘疾病的候选药物。GrEDeL首先利用生物医学摘要中的关系构造生物医学知识图谱。再通过图嵌入方法将该知识图谱中的实体和关系映射到低维向量空间中。接着通过已知的药物疾病治疗关系训练一个循环神经网络作为药物发现模型。最后通过该药物发现模型从生物医学文献中挖掘疾病的潜在治疗药物。实验结果表明本文方法可以有效的从文献中挖掘新的药物,并且可以提供该药物相关的作用机制解释。本文方法可以作为目前传统的药物发现方法的一个补充方法。