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Updated: Jul 21, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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使用边缘化图核进行可解释的分子性质预测

Yan Xiang1, Yu-Hang Tang2, Guang Lin3

  • 1Department of Biomedical Engineering, Duke University, Durham, North Carolina 27705, United States.

Journal of chemical information and modeling
|July 28, 2023
PubMed
概括

我们开发了与边缘化图核 (GPR-MGK) 进行高斯过程回归的新可解释性措施,以增强对分子机器学习的信任和理解. 与图形神经网络相比,GPR-MGK显示出优越的原子归属性.

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