Neural Inductive Matrix Completion for Predicting Disease-Gene Associations
In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations, and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural inductive matrix comple...
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| Language: | en |
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2018
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| Online Access: | http://hdl.handle.net/10754/627946 |