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MilGNet: A Multi-instance Learning-based Heterogeneous Graph Network for Drug repositioning by Yaowen Gu; Si Zheng; Bowen Zhang; Hongyu Kang; Jiao Li is a scholarly article available to read on EtoBox.

What is MilGNet: A Multi-instance Learning-based Heterogeneous Graph Network for Drug repositioning about?

The traditional wet-experiment-guided drug discovery is a labor-consuming and time-consuming process. Quite a few computational drug repositioning approaches have been proposed to predict potential drug-disease associations for the discovery of new indications for drugs and new therapies for diseases. Among them, heterogeneous graph neural networkbased approaches can learn drug/disease topological representations on heterogeneous graphs and then give precise inferences for unconfirmed drug-disease associations. However, the existing approaches ignored the meta-paths in the drugdisease networks which could enhance the model performance and interpretability. In this study, we first proposed a multiinstance learning-based heterogeneous graph network approach for drug-disease association prediction, which is called MilGNet. Fusing with heterogeneous graph convolutional layer, the MilGNet learns meta-path-level representations for given drug-disease pairs by a novel pseudo meta-path instance generator and a bidirectional translating embedding projector. Then, an attention-based multi-scale interpretable joint predictor is assembled for precise and rational drug-disease association predi

Author

Yaowen Gu; Si Zheng; Bowen Zhang; Hongyu Kang; Jiao Li

Publisher

IEEE

Published

2022

Language

EN