Curvature-enhanced graph convolutional network for biomolecular interaction prediction
Geometric deep learning has demonstrated a great potential in non-Euclidean data analysis. The incorporation of geometric insights into learning architecture is vital to its success. Here we propose a curvature-enhanced graph convolutional network (CGCN) for biomolecular interaction prediction. Our...
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Main Authors: | , , , , , |
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格式: | Article |
語言: | English |
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2024
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在線閱讀: | https://hdl.handle.net/10356/174927 |
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機構: | Nanyang Technological University |
語言: | English |