点击次数：

论文类型：期刊论文

发表时间：2019-01-01

发表刊物：INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS

收录刊物：SCIE

卷号：22

期号：2

页面范围：113-130

ISSN号：1748-5673

关键字：chemical-protein interaction; data mining; relation extraction; HNN; hierarchical neural network; RCNN; recurrent convolutional neural network

摘要：Mining chemical-protein interactions between chemicals and proteins plays vital roles in biomedical tasks, such as knowledge graph, pharmacology, and clinical research. Although chemical-protein interactions can be manually curated from the biomedical literature, the process is difficult and time-consuming. Hence, it is of great value to automatically obtain the chemical-protein interactions from biomedical literature. Recently, the most popular methods are based on the neural network to avoid complex manual processing. However, the performance is usually limited because of the lengthy and complicated sentences. To address this limitation, we propose a novel model, Hierarchical Recurrent Convolutional Neural Network (HRCNN), to learn hidden semantic and syntactic features from sentence sub-sequences effectively. Our approach achieves an F-score of 65.56% on the CHEMPROT corpus and outperforms the state-of-the-art systems. The experimental results demonstrate that our approach can greatly alleviate the defect of existing methods due to the existence of long sentences.