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- W4228996904 abstract "Drug-drug interactions are one of the main concerns in drug discovery. Accurate prediction of drug-drug interactions plays a key role in increasing the efficiency of drug research and safety when multiple drugs are co-prescribed. With various data sources that describe the relationships and properties between drugs, the comprehensive approach that integrates multiple data sources would be considerably effective in making high-accuracy prediction. In this paper, we propose a Deep Attention Neural Network based Drug-Drug Interaction prediction framework, abbreviated as DANN-DDI, to predict unobserved drug-drug interactions. First, we construct multiple drug feature networks and learn drug representations from these networks using the graph embedding method; then, we concatenate the learned drug embeddings and design an attention neural network to learn representations of drug-drug pairs; finally, we adopt a deep neural network to accurately predict drug-drug interactions. The experimental results demonstrate that our model DANN-DDI has improved prediction performance compared with state-of-the-art methods. Moreover, the proposed model can predict novel drug-drug interactions and drug-drug interaction-associated events." @default.
- W4228996904 created "2022-05-08" @default.
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- W4228996904 date "2023-03-01" @default.
- W4228996904 modified "2023-10-18" @default.
- W4228996904 title "Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks" @default.
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- W4228996904 doi "https://doi.org/10.1109/tcbb.2022.3172421" @default.
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