药品
超图
计算机科学
平滑的
人工神经网络
人工智能
机器学习
组合数学
数学
医学
药理学
计算机视觉
作者
Anh Nguyen Duc,Canh Hao Nguyen,Hiroshi Mamitsuka
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-6
被引量:4
标识
DOI:10.1109/tnnls.2023.3261860
摘要
Predicting drug-drug interactions (DDIs) is the problem of predicting side effects (unwanted outcomes) of a pair of drugs using drug information and known side effects of many pairs. This problem can be formulated as predicting labels (i.e., side effects) for each pair of nodes in a DDI graph, of which nodes are drugs and edges are interacting drugs with known labels. State-of-the-art methods for this problem are graph neural networks (GNNs), which leverage neighborhood information in the graph to learn node representations. For DDI, however, there are many labels with complicated relationships due to the nature of side effects. Usual GNNs often fix labels as one-hot vectors that do not reflect label relationships and potentially do not obtain the highest performance in the difficult cases of infrequent labels. In this brief, we formulate DDI as a hypergraph where each hyperedge is a triple: two nodes for drugs and one node for a label. We then present CentSmoothie , a hypergraph neural network (HGNN) that learns representations of nodes and labels altogether with a novel "central-smoothing" formulation. We empirically demonstrate the performance advantages of CentSmoothie in simulations as well as real datasets.
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