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在异质图上进行深度多实例学习,用于药物-疾病关联预测

Yaowen Gu1, Si Zheng2, Bowen Zhang3

  • 1Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS&PUMC), Beijing, 100020, China; Department of Chemistry, New York University, NY, 10027, USA.

Computers in biology and medicine
|November 22, 2024
PubMed
概括

通过使用深度多重实例学习来预测药物疾病关联 (DDAs),MilGNet增强了药物重新定位. 这种新的方法通过从异质网络中的路径实例中学习来提高准确性和可解释性.

关键词:
药物重新定位 药物重新定位药物疾病关联预测预测.不同质的图形神经网络的神经网络.的元路径.多个实例的学习是多个实例的学习.

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