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Updated: May 28, 2025

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SEGT-GO:一种基于PPI序列化和解释性人工智能的图形转换器方法,用于蛋白质功能预测.

Yansong Wang1, Yundong Sun1,2, Baohui Lin3

  • 1School of Computer Science and Technology, Harbin Institute of Technology Weihai Campus, Weihai, 264209, China.

BMC bioinformatics
|February 10, 2025
PubMed
概括

通过串行多跳蛋白-蛋白相互作用 (PPI) 网络信息和使用可解释的AI,SEGT-GO增强了蛋白质功能预测. 这种图形转换器方法提高了跨物种的准确性和概括性.

关键词:
可解释的人工智能图形变压器 图形变压器多点跳社区序列化多点跳社区序列化个人个人信息网络 (PPI) 网络.蛋白质功能的预测和预测

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