SWnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures

SWnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures

Background: One of the major challenges in precision medicine is accurate prediction of individual patient’s response to drugs. A great number of computational methods have been developed to predict compounds activity using genomic profiles or chemical structures, but more exploration is yet to be d...

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Bibliographic Details
Main Authors: Chen, X. (Author), Ge, H. (Author), Qian, D. (Author), Tian, L. (Author), Wang, P. (Author), Zuo, Z. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
Accurate prediction
antineoplastic agent
Antineoplastic Agents
Attention functions
Cancer cell lines
Cell culture
Cheminformatics
Convolution
Convolutional neural networks
Deep learning
Deep Learning
Diseases
drug
Drug response
Forecasting
Gene expression
Genetic mutations
genetics
genomics
Genomics
Graph neural networks
human
Humans
Learning systems
Molecular graphs
neoplasm
Neoplasms
Pharmaceutical Preparations
Predictive performance
Structure (composition)
Training and testing
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