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- W4285606639 abstract "In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction, and combination therapy design tasks have been proposed. Here, we present a unified theoretical view of relational machine learning models which can address these tasks. We provide fundamental definitions, compare existing model architectures and discuss performance metrics, datasets, and evaluation protocols. In addition, we emphasize possible high-impact applications and important future research directions in this domain." @default.
- W4285606639 created "2022-07-16" @default.
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- W4285606639 date "2022-07-01" @default.
- W4285606639 modified "2023-09-30" @default.
- W4285606639 title "A Unified View of Relational Deep Learning for Drug Pair Scoring" @default.
- W4285606639 doi "https://doi.org/10.24963/ijcai.2022/777" @default.
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