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论文类型：期刊论文

发表时间：2019-04-09

发表刊物：BMC MEDICAL INFORMATICS AND DECISION MAKING

收录刊物：PubMed、SCIE、CPCI-S

卷号：19

期号：Suppl 2

页面范围：59

ISSN号：1472-6947

关键字：Literature-based discovery; Drug discovery; Knowledge graph; Path ranking algorithm; Convolutional neural network

摘要：BackgroundDrug development is an expensive and time-consuming process. Literature-based discovery has played a critical role in drug development and may be a supplementary method to help scientists speed up the discovery of drugs.MethodsHere, we propose a relation path features embedding based convolutional neural network model with attention mechanism for drug discovery from literature, which we denote as PACNN. First, we use predications from biomedical abstracts to construct a biomedical knowledge graph, and then apply a path ranking algorithm to extract drug-disease relation path features on the biomedical knowledge graph. After that, we use these drug-disease relation features to train a convolutional neural network model which combined with the attention mechanism. Finally, we employ the trained models to mine drugs for treating diseases.ResultsThe experiment shows that the proposed model achieved promising results, comparing to several random walk algorithms.ConclusionsIn this paper, we propose a relation path features embedding based convolutional neural network with attention mechanism for discovering potential drugs from literature. Our method could be an auxiliary method for drug discovery, which can speed up the discovery of new drugs for the incurable diseases.