title: A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction.
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 (1) only takes the sdp and word embedding as input and (2) 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.
authors: Department of Computer and Information Sciences, Hefei University of Technology, Hefei 230009, China.
authors: Lei
authors: 0000-0003-2630-4080
authors: L
authors: Hua
authors: Department of Computer and Information Sciences, Kobe University, Kobe 6578501, Japan.
authors: Chanqin
authors: C
authors: Quan
mesh_terms: D000465:Algorithms; D035843:Biomedical Research / Q000379:methods; D030562:Databases, Protein; D000069550:Machine Learning; D016571:Neural Networks, Computer; D060066:Protein Interaction Maps / Q000502:physiology*; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins