Abstract
Drug–Drug interaction (DDI) prediction is essential in pharmaceutical research and clinical application. Existing computational methods mainly extract data from multiple resources and treat it as binary classification. However, this cannot unambiguously tell the boundary between positive and negative samples owing to the incompleteness and uncertainty of derived data. A granular computing method called three-way decision is proved to be effective in making uncertain decision, but it relies on supplementary information to make delay decision. Recently, biomedical knowledge graph has been regarded as an important source to obtain abundant supplementary information about drugs. This paper proposes a three-way decision-based method called 3WDDI, in combination with knowledge graph embedding as supplementary features to enhance DDI prediction. The drug pairs are divided into positive, negative and boundary regions by Convolutional Neural Network (CNN) according to drug chemical structure feature. Further, delay decision is made for objects in the boundary region by integrating knowledge graph embedding feature to promote the accuracy of decision-making. The empirical results show that 3WDDI yields up to 0.8922, 0.9614, 0.9582, 0.8930 for Accuracy, AUPR, AUC and F1-score, respectively, and outperforms several baseline models.
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Acknowledgements
The work reported in this paper was partially supported by a National Natural Science Foundation of China project 61963004 and 62072124, a key project of Natural Science Foundation of Guangxi 2017GXNSFDA198033, and a key research and development plan of Guangxi AB17195055.
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Hao, X., Chen, Q., Pan, H. et al. Enhancing drug–drug interaction prediction by three-way decision and knowledge graph embedding. Granul. Comput. 8, 67–76 (2023). https://doi.org/10.1007/s41066-022-00315-4
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DOI: https://doi.org/10.1007/s41066-022-00315-4