论文标题
多剂量副作用预测的三画信息传播
Tri-graph Information Propagation for Polypharmacy Side Effect Prediction
论文作者
论文摘要
药物组合的使用通常会导致多药副作用（姿势）。最近的一种方法将姿势预测作为链接预测问题在药物和蛋白质图上的链接预测问题，并使用图形卷积网络（GCN）解决。但是，由于姿势的复杂关系，该方法具有很高的计算成本和内存需求。本文提出了一个灵活的三学信息传播（TIP）模型，该模型在三个子图上运行，通过从蛋白质 - 蛋白质图到药物 - 药物图逐渐地学习表示形式。实验表明，TIP将准确性提高了7％以上，时间效率提高了83 $ \ times $，而空间效率则提高了3 $ \ times $。
The use of drug combinations often leads to polypharmacy side effects (POSE). A recent method formulates POSE prediction as a link prediction problem on a graph of drugs and proteins, and solves it with Graph Convolutional Networks (GCNs). However, due to the complex relationships in POSE, this method has high computational cost and memory demand. This paper proposes a flexible Tri-graph Information Propagation (TIP) model that operates on three subgraphs to learn representations progressively by propagation from protein-protein graph to drug-drug graph via protein-drug graph. Experiments show that TIP improves accuracy by 7%+, time efficiency by 83$\times$, and space efficiency by 3$\times$.
