Title: Chemical-protein interaction extraction from biomedical literature: a hierarchical recurrent convolutional neural network method

Authors: Cong Sun; Zhihao Yang; Lei Wang; Yin Zhang; Hongfei Lin; Jian Wang; Liang Yang; Kan Xu; Yijia Zhang

Addresses: College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China ' College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China ' Beijing Institute of Health Administration and Medical Information, Beijing, 100850, China ' Beijing Institute of Health Administration and Medical Information, Beijing, 100850, China ' College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China ' College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China ' College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China ' College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China ' College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China

Abstract: 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.

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

DOI: 10.1504/IJDMB.2019.099725

International Journal of Data Mining and Bioinformatics, 2019 Vol.22 No.2, pp.113 - 130

Received: 03 Apr 2019
Accepted: 03 Apr 2019
Published online: 20 May 2019 *

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