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• W4319348642 abstract "We have developed a graphical convolutional network (GCN)-based deep learning model to predict the effect of a drug on cancer cells, which integrates multi-source information such as the gene interaction networks of human cells, the structure of drug molecules, and the gene expressions induced by drugs. In the model, genes, cells, even drug effects are all represent by 1024-dimensional vectors. Based on the vector representations, we develop a deep drug effector predictor (DDEP), which is essentially a set of GCNs encoding the effects of a drug, with the input of the structure, dosage, and duration of the drug. We found that DDEP can predict drug efficacy with accuracy far better than that achieved by simple drug/target classification, and the vector representations grasp well the comprehensive states of a cell." @default.
• W4319348642 created "2023-02-08" @default.
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• W4319348642 date "2023-01-01" @default.
• W4319348642 modified "2023-09-27" @default.
• W4319348642 title "Drug Effect Deep Learner Based on Graphical Convolutional Network" @default.
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• W4319348642 doi "https://doi.org/10.1007/978-3-031-20730-3_4" @default.
• W4319348642 hasPublicationYear "2023" @default.
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