Title: KeSACNN: a protein-protein interaction article classification approach based on deep neural network

Authors: Ling Luo; Zhihao Yang; Lei Wang; Yin Zhang; Hongfei Lin; Jian Wang

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

Abstract: Automatic classification of protein-protein interaction (PPI) relevant articles from biomedical literature is a crucial step for biological database curation since it can help reduce the curation burden at the initial stage. However, most popular PPI article classification methods are based on traditional machine learning and their performances are heavily dependent on the feature engineering. Recent years, PPI article classification with neural networks has gained increasing attention, but domain knowledge has been rarely used in these methods. Aiming to exploit domain knowledge, we propose a domain Knowledge-enriched Self-Attention Convolutional Neural Network (KeSACNN) approach for PPI article classification. In this approach, two knowledge embeddings are proposed, and the novel convolution neural network architectures with self-attention mechanism are designed to leverage biomedical knowledge. The experimental results show that our method achieves the state-of-the-art performance on the BioCreative II and III corpora (82.92% and 67.93% in F-scores, respectively).

Keywords: PPI article classification; self-attention; convolutional neural network; domain knowledge.

DOI: 10.1504/IJDMB.2019.099724

International Journal of Data Mining and Bioinformatics, 2019 Vol.22 No.2, pp.131 - 148

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

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