通过嵌入三向决策和知识图的三向决策和知识图来提高药物相互作用预测
Xinkun Hao1,2, Qingfeng Chen1,3,2, Haiming Pan1,2
1School of Computer, Electronics and Information, Guangxi University, Nanning, 530004 Guangxi China.
概括
这项研究介绍了3WDDI,这是一种用于药物相互作用 (DDI) 预测的新方法. 通过将三向决策与知识图嵌入相结合,它提高了识别潜在DDI的准确性.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 药物相互作用 (DDI) 的预测对于制药研究和临床实践至关重要.
- 当前的计算方法经常与数据不完整和不确定性作斗争,导致DDI的模两可的分类.
- 生物医学知识图提供了丰富的补充信息,有价值用于提高DDI预测.
研究的目的:
- 开发一种先进的计算方法,用于更准确的DDI预测.
- 解决现有的DDI预测模型中二进制分类的局限性.
- 利用生物医学知识图表和三向决策来改进DDI识别.
主要方法:
- 提出了一种基于三向决策的方法 (3WDDI),将知识图嵌入作为补充功能.
- 卷积神经网络 (CNN) 用于根据化学结构特征将药物对分为正,负和边界区域.
- 边界区域样本的延迟决定是通过结合知识图嵌入功能来做出的.
主要成果:
- 3WDDI方法实现了高性能指标:精度 (0.8922),AUPR (0.9614),AUC (0.9582) 和F1得分 (0.8930).
- 知识图嵌入的整合显著提高了DDI预测的准确性.
- 与几个基线DDI预测模型相比,3WDDI表现优越.
结论:
- 拟议的3WDDI方法通过使用三向决策和知识图嵌入,有效地提高了药物相互作用预测的准确性.
- 这种方法成功地解决了传统DDI预测方法固有的不确定性和不完整性问题.
- 3WDDI代表了计算DDI预测的重大进步,为制药研究和临床应用提供了更高的可靠性.
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