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半监督异质图对比学习用于药物向相互作用预测
Kainan Yao1, Xiaowen Wang1, Wannian Li2
1School of Software Engineering, Tongji University, 4800 Caoan Road, Jiading District, Shanghai, 201804, China.
Computers in biology and medicine
|July 8, 2023
概括
这项研究引入了SHGCL-DTI,一种使用自我监督对比学习来增强药物向相互作用 (DTI) 预测的新框架. 该方法提高了准确性和概括性,即使数据有限,也有助于药物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 机器学习是机器学习.
背景情况:
- 药物向相互作用 (DTI) 对于药物发现和重新定位至关重要.
- 基于图形的方法对DTI预测有前途,但在有限的标记数据方面存在困难.
- 有限的数据减少了现有的DTI预测模型的概括能力.
研究的目的:
- 为改进DTI预测提出一个新的框架,SHGCL-DTI.
- 利用自我监督的对比学习来克服DTI预测中的数据限制.
- 提高DTI预测模型的概括能力.
主要方法:
- 开发了SHGCL-DTI,该框架将半监督的DTI预测与图形对比学习相结合.
- 使用邻居和元路径视图生成节点表示.
- 定义了正负对,以最大限度地提高对比学习的不同观点之间的相似性.
- 重建异质网络以预测潜在的DTI.
主要成果:
- 与最先进的方法相比,SHGCL-DTI在各种场景中显示了DTI预测的显著改善.
- 废除研究证实,对比学习模块增强了预测性能和概括性.
- 通过现有生物学文献支持的新型DTI.
结论:
- 通过自主监督对比学习,SHGCL-DTI有效地改善了药物向相互作用预测.
- 拟议的框架提供了增强的概括能力,这对于药物发现至关重要.
- 该方法成功地识别了新的,生物相关的DTI.
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