دورية أكاديمية
Curvature-enhanced graph convolutional network for biomolecular interaction prediction
| العنوان: | Curvature-enhanced graph convolutional network for biomolecular interaction prediction |
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| المؤلفون: | Shen, Cong, Ding, Pingjian, Wee, Junjie, Bi, Jialin, Luo, Jiawei, Xia, Kelin |
| المساهمون: | School of Physical and Mathematical Sciences |
| سنة النشر: | 2024 |
| المجموعة: | DR-NTU (Digital Repository at Nanyang Technological University, Singapore) |
| مصطلحات موضوعية: | Mathematical Sciences, Ollivier-Ricci curvature, Graph convolutional network |
| الوصف: | 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 CGCN employs Ollivier-Ricci curvature (ORC) to characterize network local geometric properties and enhance the learning capability of GCNs. More specifically, ORCs are evaluated based on the local topology from node neighborhoods, and further incorporated into the weight function for the feature aggregation in message-passing procedure. Our CGCN model is extensively validated on fourteen real-world bimolecular interaction networks and analyzed in details using a series of well-designed simulated data. It has been found that our CGCN can achieve the state-of-the-art results. It outperforms all existing models, as far as we know, in thirteen out of the fourteen real-world datasets and ranks as the second in the rest one. The results from the simulated data show that our CGCN model is superior to the traditional GCN models regardless of the positive-to-negative-curvature ratios, network densities, and network sizes (when larger than 500). ; Ministry of Education (MOE) ; Nanyang Technological University ; Published version ; This work was supported in part by the National Natural Science Foundation of China (NSFC grant nos. 61873089, 62032007), Nanyang Technological University SPMS Collaborative Research Award 2022, Singapore Ministry of Education Academic Research fund (RG16/23, MOE-T2EP20120-0013, MOE-T2EP20220-0010, MOE-T2EP20221- 0003) and China Scholarship Council (CSC grant no. 202006130147). |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2001-0370 |
| العلاقة: | RG16/23; MOE-T2EP20120-0013; MOE-T2EP20220-0010; MOE-T2EP20221-0003; Computational and Structural Biotechnology Journal; Shen, C., Ding, P., Wee, J., Bi, J., Luo, J. & Xia, K. (2024). Curvature-enhanced graph convolutional network for biomolecular interaction prediction. Computational and Structural Biotechnology Journal, 23, 1016-1025. https://dx.doi.org/10.1016/j.csbj.2024.02.006Test; https://hdl.handle.net/10356/174927Test; 2-s2.0-85185555360; 23; 1016; 1025 |
| DOI: | 10.1016/j.csbj.2024.02.006 |
| الإتاحة: | https://doi.org/10.1016/j.csbj.2024.02.006Test https://hdl.handle.net/10356/174927Test |
| حقوق: | © 2024 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0Test/). |
| رقم الانضمام: | edsbas.4E9F656C |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 20010370 |
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| DOI: | 10.1016/j.csbj.2024.02.006 |