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BioMed Research International
Volume 2016 (2016), Article ID 8479587, 9 pages
http://dx.doi.org/10.1155/2016/8479587
Research Article

A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction

1Department of Computer and Information Sciences, Hefei University of Technology, Hefei 230009, China
2Department of Computer and Information Sciences, Kobe University, Kobe 6578501, Japan

Received 4 March 2016; Revised 4 June 2016; Accepted 15 June 2016

Academic Editor: Rita Casadio

Copyright © 2016 Lei Hua and Chanqin Quan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method only takes the sdp and word embedding as input and could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task.