基于实体信息和图神经网络的药物相互作用关系抽取
作者:
作者单位:

1.四川大学计算机学院;2.四川民族学院理工学院

作者简介:

通讯作者:

中图分类号:

TP391

基金项目:

国家自然科学基金(61972270); 四川省重点研发项目(2019YFG0521)


Drug-Drug relationship extraction based on entity information and graph neural networks
Author:
Affiliation:

1.College of Computer Science, Sichuan University;2.College of Science and Technology, Sichuan University for Nationalities;3.Sichuan University College of Computer Science

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    药物相互作用是指药物与药物之间相互促进或抑制.针对现有的药物关系抽取方法利用外部背景知识和自然语言处理工具导致错误传播和积累的问题,以及现有大多数研究在数据预处理阶段 对药物实体进行盲化,忽略了有助于识别关系类别的目标药物实体信息的问题.论文提出了基于预训练生物医学语言模型和词汇图神经网络的药物相互作用关系抽取模型,该模型通过预训练语 言模型获得句子的原始特征表示,在基于数据集构建的词汇图上进行卷积操作获得与句子相关的全局特征信息表示,最后与药物目标实体对特征进行拼接从而构建药物相互作用关系提取任务 的特征表示,在获得丰富的全局特征信息的同时避免了使用自然语言处理工具和外部背景知识,提升模型的准确率.论文的模型在DDIExtraction 2013数据集上的F1值达到了83.25% ,优于目前最新方法2.35%.

    Abstract:

    Drug-Drug interaction refers to the mutual promotion or inhibition between drugs. For the existing drug relationship extraction methods, the use of external background knowledge and natural language processing tools leads to the problem of error propagation and accumulation, and most existing studies blind drug entities at the data preprocessing stage, ignoring the target drug entity information that is helpful to identify the relationship category. In this paper, a drug interaction extraction model based on pretrained biomedical language model and word map neural network is proposed. In this model, the original feature representation of sentences is obtained by pretrained language model, and the global feature information representation of sentences is obtained by convolution operation on the word map constructed based on data set. Finally, the feature representation of drug interaction relationship extraction task was constructed by stitching the feature with drug target entities, which can not only obtain rich global feature information but also avoid using natural language processing tools and external background knowledge, and improve the accuracy of the model. The F1 value of the model on the DDIExtraction 2013 dataset achieved 83.25%, which outperforms the current latest methods by 2.35%.

    参考文献
    相似文献
    引证文献
引用本文

引用本文格式: 杨霞,韩春燕,琚生根. 基于实体信息和图神经网络的药物相互作用关系抽取[J]. 四川大学学报: 自然科学版, 2022, 59: 022002.

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-09-29
  • 最后修改日期:2021-10-18
  • 录用日期:2021-10-22
  • 在线发布日期: 2022-04-01
  • 出版日期: