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- W4306641104 abstract "Abstract Drug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increased rapidly. In this study, we proposed a deep learning model called the Dual Feature Fusion Network for Drug-Drug Synergy prediction (DFFNDDS) that utilizes a fine-tuned pretrained language model and dual feature fusion mechanism to predict synergistic drug combinations. The dual feature fusion mechanism fuses the drug features and cell line features at the bit-wise level and the vector-wise level. We demonstrated that DFFNDDS outperforms competitive methods and can serve as a reliable tool for identifying synergistic drug combinations." @default.
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- W4306641104 date "2022-10-17" @default.
- W4306641104 modified "2023-10-18" @default.
- W4306641104 title "DFFNDDS: Prediction of Synergistic Drug Combinations With Dual Feature Fusion Networks" @default.
- W4306641104 doi "https://doi.org/10.21203/rs.3.rs-2148959/v1" @default.
- W4306641104 hasPublicationYear "2022" @default.
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