论文标题
蛋白质 - 蛋白质相互作用提取的知识吸引注意力网络
Knowledge-aware Attention Network for Protein-Protein Interaction Extraction
论文作者
论文摘要
蛋白质蛋白质相互作用（PPI）从已发表的科学文献中提取为精确医学工作提供了更多支持。但是，许多当前的PPI提取方法需要广泛的功能工程，并且无法充分利用知识库（KB）中的先验知识。 KB包含有关实体和关系的大量结构化信息，因此在PPI提取中起关键作用。本文提出了一个知识吸引的注意网络（KAN），以融合有关PPI提取的蛋白质 - 蛋白质对和上下文信息的先验知识。提出的模型首先采用对角线降低的多头注意机制来编码上下文序列以及从KB中学到的知识表示。然后，使用一种新颖的多维注意机制来选择可以最好地描述编码环境的功能。实验结果对生物歧视性VI PPI数据集表明，所提出的方法可以在序列中获得不同单词之间的知识依赖性，并导致新的最新性能。
Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. However, many of the current PPI extraction methods need extensive feature engineering and cannot make full use of the prior knowledge in knowledge bases (KB). KBs contain huge amounts of structured information about entities and relationships, therefore plays a pivotal role in PPI extraction. This paper proposes a knowledge-aware attention network (KAN) to fuse prior knowledge about protein-protein pairs and context information for PPI extraction. The proposed model first adopts a diagonal-disabled multi-head attention mechanism to encode context sequence along with knowledge representations learned from KB. Then a novel multi-dimensional attention mechanism is used to select the features that can best describe the encoded context. Experiment results on the BioCreative VI PPI dataset show that the proposed approach could acquire knowledge-aware dependencies between different words in a sequence and lead to a new state-of-the-art performance.
