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Updated: Jan 8, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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药物相互作用的多种分类基于一个完整的图形卷积神经网络和可解释的人工智能.

Samar Monem1,2, Ashraf Darwish2,3,4,5, Aboul Ella Hassanien2,6

  • 1Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni Suef, Egypt.

Journal of bioinformatics and computational biology
|December 17, 2025
PubMed
概括

这项研究引入了一个图形卷积神经网络 (GCN) 模型来预测药物相互作用 (DDI),达到95.12%的准确性. 可解释的人工智能方法通过识别潜在的药物危害,提高了多药疗法的安全性.

关键词:
药品银行 药品银行药物药物相互作用 (DDI) 是一种药物相互作用.沙普利添加剂解释 (SHAP)可解释的人工智能 (XAI)图形卷积神经 (GCN) 的一个例子.多种分类的多种分类.

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