SMGCN:基于多重相似性和多个内核融合的图形卷积神经网络用于药物向相互作用的预测
IEEE/ACM transactions on computational biology and bioinformatics
|December 5, 2023
概括
这项研究介绍了SMGCN,这是一种新的图形卷积网络 (GCN) 方法,用于预测药物向相互作用 (DTI). 通过整合多个相似度和多个内核学习,SMGCN提高了DTI预测的准确性,加速了药物发现.
科学领域:
- 生物信息学是一种生物信息学.
- 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:
- 准确识别药物向相互作用 (DTI) 对于加速药物发现至关重要.
- 尽管进行了广泛的研究,但预测DTI仍然是一个复杂的挑战.
- 现有的方法往往难以捕捉复杂的网络结构和关系.
研究的目的:
- 提出一种新的计算方法,SMGCN,用于增强DTI的预测.
- 利用图形卷积网络 (GCN) 结合多重相似性和多重内核学习.
- 提高识别潜在药物标关系的准确性和效率.
主要方法:
- 开发了SMGCN,与GCN集成多重相似度 (随机步行与重启,等号相似度) 和多重内核学习 (MKL).
- 使用GCN从网络结构中提取多层,低维的嵌入特征.
- 使用双拉普拉斯调节最小方程来通过组合内核预测新的DTI.
主要成果:
- 在金色标准数据集上,SMGCN在预测DTI方面取得了显著的改进.
- 该模型实现了更高的性能指标,包括曲线下的面积 (AUC) 和精度召回曲线下的面积 (AUPR).
- 拟议的方法成功识别了新的DTI,并与KEGG BRITE数据库和科学文献进行了验证.
结论:
- SMGCN提供了一种强大而有效的方法来预测药物向相互作用.
- 多重相似性和MKL的整合增强了GCN在DTI预测中的能力.
- 这种方法有望通过识别新的治疗点来加速药物发现管道.
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