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论文类型：会议论文

发表时间：2015-11-09

收录刊物：EI、CPCI-S、Scopus

页面范围：1156-1156

关键字：Deep learning; biomedical text mining; interaction extraction; neural network

摘要：This paper presents a deep neural network-based protein-protein interactions (PPIs) information extraction approach which can learn complex and abstract features automatically from unlabeled data by unsupervised representation learning methods. This approach first employs the training algorithm of auto-encoders to initialize 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. In addition, the performance comparison with APG also verifies the effectiveness of our method.