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• W2937594636 abstract "Efficient representations of drugs provide important support for healthcare analytics, such as drug–drug interaction (DDI) prediction and drug–drug similarity (DDS) computation. However, incomplete annotated data and drug feature sparseness create substantial barriers for drug representation learning, making it difficult to accurately identify new drug properties prior to public release. To alleviate these deficiencies, we propose KMR, a knowledge-oriented feature-driven method which can learn drug related knowledge with an accurate representation. We conduct series of experiments on real-world medical datasets to demonstrate that KMR is capable of drug representation learning. KMR can support to discover meaningful DDI with an accuracy rate of 92.19%, demonstrating that techniques developed in KMR significantly improve the prediction quality for new drugs not seen at training. Experimental results also indicate that KMR can identify DDS with an accuracy rate of 88.7% by facilitating drug knowledge, outperforming existing state-of-the-art drug similarity measures." @default.
• W2937594636 created "2019-04-25" @default.
• W2937594636 creator A5014238603 @default.
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• W2937594636 date "2019-03-14" @default.
• W2937594636 modified "2023-10-16" @default.
• W2937594636 title "KMR: knowledge-oriented medicine representation learning for drug–drug interaction and similarity computation" @default.
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• W2937594636 doi "https://doi.org/10.1186/s13321-019-0342-y" @default.
• W2937594636 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6419809" @default.
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• W2937594636 hasPublicationYear "2019" @default.
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