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Polypharmacy Side Effect Prediction With Relational Representation Learning
Polypharmacy Side Effect Prediction With Relational Representation Learning
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机译:具有关系表示学习的多药副作用预测
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摘要
A system adapted to receive a knowledge base, which may include drug data, human biological data, drug-drug interactions, protein-protein interactions, gene expression, protein and drug interaction data, genotypic information for cell lines, drug side effects, and disease classification labels. The system may generate a knowledge graph based on the knowledge base, and convert the knowledge graph into embeddings that include points in a k-dimensional metric space. The system may determine a medical effect weighting based on a drug combination query, and update the embeddings of the drug combination. The system may utilize a pooling method to update predicate embeddings. The system may determine polypharmacy scores for the embeddings, and rank the predicted links between a drug combination and side effects.
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