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Updated: Jun 8, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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GGN-GO:通过多尺度结构特征预测蛋白质功能的几何图形网络

Jia Mi1, Han Wang1, Jing Li2

  • 1The College of Information Science and Technology, Beijing University of Chemical Technology, Beijing.

Briefings in bioinformatics
|November 1, 2024
PubMed
概括

这项研究引入了一种新的几何图形网络 (GGN-GO) 来预测蛋白质功能,通过捕获原子级结构细节来提高准确性. 该方法增强了蛋白质功能注释,克服了现有的深度学习方法的局限性.

关键词:
几何图形网络是几何图形网络.图表注意力聚合集中的情况.图表对比的学习学习.多个尺度的结构特征.蛋白质功能的预测和预测.

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