HeteroKGRep:基于异质知识图的药物重新定位
Ribot Fleury T Ceskoutsé1, Alain Bertrand Bomgni2,3, David R Gnimpieba Zanfack4
1Ecole Nationale Supérieure Polytechnique, University of Yaounde I, P.O. Box. 8390, Yaoundé, Cameroon.
概括
这项研究介绍了HeteroKGRep,一种使用多种生物医学数据的新型药物重新定位模型. 它有效地确定了现有药物的新治疗用途,提高了药物发现效率.
科学领域:
- 生物医学信息学是生物医学信息学.
- 计算药理学是一种计算药理学.
- 药物发现 药物发现
背景情况:
- 药物开发是漫长而昂贵的,专利复杂性阻碍了创新.
- 现有的药物重新定位模型通常依赖于有限的,均的数据源.
- 异质生物医学知识图为药物重新定位提供了更丰富的数据景观.
研究的目的:
- 提出 HeteroKGRep,一种新的药物重新定位模型,利用异构的生物医学知识图.
- 克服以前依赖于均数据的模型的局限性.
- 加强对现有药物的新疗法应用的发现.
主要方法:
- HeteroKGRep采用多步框架,包括相似度图生成和SMOTE过量采样.
- 它使用异质图神经网络来生成节点序列.
- 药物和疾病嵌入被提取用于预测重新利用机会.
主要成果:
- HeteroKGRep以99%的精度,95%的AUC ROC和94%的平均精度实现了最先进的性能.
- 该模型有效地利用多种知识源来丰富表达式学习.
- 与现有的均质方法相比,它表现出优越的性能.
结论:
- HeteroKGRep为以知识为导向的药物重新定位建立了一个有希望的新范式.
- 该模型可以发现新的药物疾病关联,补充新的药物开发.
- 在异质图表中利用多模式生物医学数据可以增强药物重定向策略.
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