3260 papers • 126 benchmarks • 313 datasets
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A scalable and robust deep learning framework to learn embedded representations to unify known gene interactions and gene expression for gene interaction predictions and demonstrates the importance of integrating heterogeneous information about genes for gene network inference.
This work proposes a novel Communicative Subgraph representation learning for Multi-relational Inductive drug-Gene interactions prediction (CoSMIG), where the predictions of drug-gene relations are made through subgraph patterns, and thus are naturally inductive for unseen drugs/genes without retraining or utilizing external domain features.
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