A protein-protein interaction extraction approach based on deep neural network. (2016)

• Record Type: Journal Article
• Authors: Zhao, Zhehuan; Yang, Zhihao; Lin, Hongfei; Wang, Jian; Gao, Song
• Is Part Of: International journal of data mining and bioinformatics. Volume 15:Number 2(2016)
• Page Start: 145
• Page End: 164
• Publication Date: 2016
• Subjects: deep learning -- biomedical literature -- text mining -- PPI extraction -- neural networks -- protein-protein interactions -- bioinformatics -- unlabelled data -- unsupervised learning; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

Protein-Protein Interactions (PPIs) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Machine learning methods have been the most popular ones in PPI extraction area. However, these methods are still feature engineering-based, which means that their performances are also heavily dependent on the appropriate feature selection which is still a skill-dependent task. This paper presents a deep neural network-based approach which can learn complex and abstract features automatically from unlabelled data by unsupervised representation learning methods. This approach first employs the training algorithm of auto-encoders to initialise the parameters of a deep multilayer neural network. Then the gradient descent method using back propagation is applied to train this deep multilayer neural network model. Experimental results on five public PPI corpora show that our method can achieve better performance than can a multilayer neural network: on two 'toughest handling' corpora AImed and BioInfer, the former outperforms the latter with the improvements of 3.10 and 2.89 percentage units in F-score, respectively. In addition, the performance comparison with APG also verifies the effectiveness of our method.