Abstract
Drug-drug interactions occur when two or more drugs are taken simultaneously or successively. Early discovery of drug-drug interactions can effectively prevent medical accidents and reduce medical costs. There are already many methods to discover drug-drug interactions. However, the current methods still have much space for performance improvement. We propose a new deep learning approach named FM-DDI based on feature reconstruction and multi-dimensional attention mechanism for drug-drug interactions prediction. The feature reconstruction extracts low-dimensional but informative vector representations of features for the drug from heterogeneous data sources, which can prevent information loss. The deep neural network model based on multi-dimensional attention mechanism gives high weight to critical feature dimensions, which can effectively learn critical information. FM-DDI achieves substantial performance improvement over several state-of-the-art methods for drug-drug interaction prediction. The results indicate that FM-DDI can provide a valuable tool for extracting and learning drug features to predict new drug-drug interactions.
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Xie, J., Ouyang, J., Zhao, C., He, H., Dong, X. (2021). A Deep Learning Approach Based on Feature Reconstruction and Multi-dimensional Attention Mechanism for Drug-Drug Interaction Prediction. In: Wei, Y., Li, M., Skums, P., Cai, Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science(), vol 13064. Springer, Cham. https://doi.org/10.1007/978-3-030-91415-8_34
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