MPTN:一个从知识图表转换药物的信息传递变压器网络
Yuanxin Liu1, Guoming Sang1, Zhi Liu1
1School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China.
Computers in biology and medicine
|December 3, 2023
概括
这项研究介绍了一种新的传递信息的变压器网络 (MPTN),用于使用知识图进行药物重用. MPTN增强了语义信息提取,优于现有的知识图嵌入方法来预测新的药物指示.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 药物再利用 (DR) 利用现有药物进行新的治疗用途,通常以知识图 (KG) 为指导.
- 目前基于KG的DR方法难以从复杂的生物医学数据中提取丰富的语义信息.
- 计算和数据可用性的进步需要对DR进行改进的KG推理模型.
研究的目的:
- 提出一个新的传递信息的变压器网络 (MPTN) 改进药物重新用途.
- 改进从生物医学知识图表中的上下文三重中提取语义信息.
- 为了更准确地预测现有药物的新治疗途径.
主要方法:
- 开发了一个传递信息的变压器网络 (MPTN),集成CompGCN以嵌入聚合和基于变压器的传递信息模块.
- 整合了注意力机制,以捕捉实体三重体内的语义上下文.
- 利用剩余连接来保存信息,使用InteractE来预测异质特征交互.
主要成果:
- 拟议的MPTN模型与现有的知识图嵌入 (KGE) 方法相比,表现优越.
- 在两个数据集上的实验证实了该模型在预测新药适用性方面的有效性.
- 该MPTN模型在从生物医学KG中提取语义信息方面显示出显著的改进.
结论:
- MPTN提供了一种强大的新方法,用于基于知识图的药物重用.
- 该模型捕获上下文语义信息的能力提高了对药物的新疗法应用的预测.
- 这项工作通过提高KG推理能力,推进了计算药物发现领域.
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