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杜磊 副教授

2006年本科毕业于大连理工大学数学与应用数学专业,2008年硕士毕业于大连理工大学计算数学专业(导师:于波 教授),2011年博士毕业于日本名古屋大学计算理工学专攻,获博士(工学)学位(导师:张绍良 教授)。后在筑波大学计算机科学专攻从事博士后研究工作(日本技术振兴机构CREST项目资助,合作导师:Prof. SAKURAI Tetsuya)。2014年回国任职于大连理工大学数学科学学院。主要研究内容包括:大型稀疏线性方程组求解、矩阵特征值计算、高性能科学计算等。

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Improving neural protein-protein interaction extraction with knowledge selection

发布时间: 2019-11-23 点击次数:

  • 论文类型:期刊论文
  • 第一作者:Zhou, Huiwei
  • 通讯作者:Yao, WH (reprint author), Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Liaoning, Peoples R China.
  • 合写作者:Li, Xuefei,Yao, Weihong,Liu, Zhuang,Ning, Shixian,Lang, Chengkun,Du, Lei
  • 收录刊物:PubMed、EI、SCIE
  • 卷号:83
  • 页面范围:107146
  • ISSN号:1476-9271
  • 关键字:PPI extraction; Knowledge selection; Mutual attention; Prior knowledge
  • 摘要:Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. Meanwhile, knowledge bases (KBs) contain huge amounts of structured information of protein entities and their relations, which can be encoded in entity and relation embeddings to help PPI extraction. However, the prior knowledge of protein-protein pairs must be selectively used so that it is suitable for different contexts. This paper proposes a Knowledge Selection Model (KSM) to fuse the selected prior knowledge and context information for PPI extraction. Firstly, two Transformers encode the context sequence of a protein pair according to each protein embedding, respectively. Then, the two outputs are fed to a mutual attention to capture the important context features towards the protein pair. Next, the context features are used to distill the relation embedding by a knowledge selector. Finally, the selected relation embedding and the context features are concatenated for PPI extraction. Experiments on the BioCreative VI PPI dataset show that KSM achieves a new state-of-the-art performance (38.08 % F1-score) by adding knowledge selection.
  • 发表时间:2019-12-01