计算机科学
代表(政治)
水准点(测量)
图形
机器学习
人工智能
联想(心理学)
药物重新定位
特征学习
构造(python库)
过程(计算)
药品
理论计算机科学
操作系统
精神科
认识论
哲学
政治
政治学
程序设计语言
法学
地理
心理学
大地测量学
作者
Bo-Wei Zhao,Xiaorui Su,Yue Yang,Dongxu Li,Pengwei Hu,Zhu‐Hong You,Lun Hu
标识
DOI:10.1007/978-981-99-4749-2_14
摘要
Identifying new indications for existing drugs is a crucial role in drug research and development. Computational-based methods are normally regarded as an effective way to infer drugs with new indications. They, though effective, normally fall short of capturing semantic higher-order connectivity patterns presented in heterogeneous biological information networks (HBINs) when learning the respective embeddings of drugs and diseases. To overcome this problem, we propose a novel Multi-level Subgraph Representation Learning model, namely MSRLDDA, for drug-disease association (DDA) prediction. In particular, MSRLDDA first defines different meta-paths to construct semantic subgraphs such that the mechanisms of how drugs act on diseases can be revealed. For each subgraph, a particular graph neural network model is adopted to conduct the representation learning process from different perspectives. By doing so, more expressive representations of drugs and diseases are obtained at multi-level. Experimental results on two benchmark datasets demonstrate that MSRLDDA performs better than several state-of-the-art drug repositioning models. This is a strong indicator that the consideration of higher-order connectivity patterns gains new insight into DDA prediction with improved accuracy.
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