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

By Lei Hua, Chanqin Quan. 2016

56 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Zhiyong Lu
2. Robert Leaman
3. Chih-Hsuan Wei
4. Anita M. Preininger
5. Fei Wang
6. Rezarta Islamaj Dogan

Citations in the corpus

• Deep learning for extracting protein-protein interactions from biomedical literature (2017, methodology): “For PPI extraction, convolutional neural networks (CNN) have been adopted and applied effectively (Zeng et al., 2014; Quan et al., 2016; Hua and Quan, 2016).”