Abstract Motivation Neural methods to extract drug–drug interactions (DDIs) from literature require a large number of annotations. In this study, we propose a novel method to effectively utilize external drug database information as well as information from large-scale plain text for DDI extraction. Specifically, we focus on drug description and molecular structure information as the drug database information. Results We evaluated our approach on the DDIExtraction 2013 shared task dataset. We obtained the following results. First, large-scale raw text information can greatly improve the performance of extracting DDIs when combined with the existing model and it shows the state-of-the-art performance. Second, each of drug description and molecular structure information is helpful to further improve the DDI performance for some specific DDI types. Finally, the simultaneous use of the drug description and molecular structure information can significantly improve the performance on all the DDI types. We showed that the plain text, the drug description information and molecular structure information are complementary and their effective combination is essential for the improvement. Availability and implementation Our code is available at https://github.com/tticoin/DESC_MOL-DDIE.
This paper describes a system called STeP/spl I.bar/IN (standing for socio-technical platform for in situ networking) that assists software developer to find and learn Java API libraries. It provides individualized search interface for Java developers, examples that illustrate the usage, and more distinctively a facilitating mechanism that connects Java developers to exchange expertise based on their expertise and social relations. STeP/spl I.bar/IN is designed and developed based on the dynamic community theory, a new collaboration form that we have proposed.
We propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. We encode textual drug pairs with convolutional neural networks and their molecular pairs with graph convolutional networks (GCNs), and then we concatenate the outputs of these two networks. In the experiments, we show that GCNs can predict DDIs from the molecular structures of drugs in high accuracy and the molecular information can enhance text-based DDI extraction by 2.39 percent points in the F-score on the DDIExtraction 2013 shared task data set.
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