Article: A protein-protein interaction extraction approach based on deep neural network Journal: International Journal of Data Mining and Bioinformatics (IJDMB) 2016 Vol.15 No.2 pp.145 - 164 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. Inderscience Publishers - linking academia, business and industry through research

Title: A protein-protein interaction extraction approach based on deep neural network

Authors: Zhehuan Zhao; Zhihao Yang; Hongfei Lin; Jian Wang; Song Gao

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 ' 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 ' Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian 116023, China

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.

Keywords: deep learning; biomedical literature; text mining; PPI extraction; neural networks; protein-protein interactions; bioinformatics; unlabelled data; unsupervised learning.

DOI: 10.1504/IJDMB.2016.076534

International Journal of Data Mining and Bioinformatics, 2016 Vol.15 No.2, pp.145 - 164

Received: 02 Feb 2016
Accepted: 09 Feb 2016
Published online: 11 May 2016 *

Title: A protein-protein interaction extraction approach based on deep neural network

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