iGRLDTI:一种改进的图形表示学习方法,用于在异质生物信息网络上预测药物向相互作用
Bo-Wei Zhao1,2,3, Xiao-Rui Su1,2,3, Peng-Wei Hu1,2,3
1The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.
本研究介绍了iGRLDTI,这是一种用于预测药物向相互作用 (DTI) 的新型图形表示学习方法. 它有效地克服了图形神经网络过度平滑的问题,提高了DTI预测的准确性.
科学领域:
- 生物信息学是一种生物信息学.
- 计算机化药物发现技术
- 机器学习 机器学习
背景情况:
- 药物向相互作用 (DTI) 对于新药发现至关重要.
- 计算方法提供高效和成本效益的DTI预测.
- 图形神经网络 (GNN) 是有前途的,但在异质生物信息网络 (HBIN) 中遭受过度平滑.
研究的目的:
- 提出一种改进的图表表示学习方法,iGRLDTI,用于增强DTI预测.
- 为了解决基于GNN的DTI预测中的过度平滑问题.
- 为了捕捉更多对毒品和目标的歧视性表现.
主要方法:
- 构建一个异构的生物信息网络 (HBIN),整合药物和目标信息.
- 采用一个依赖于节点的本地平滑策略来缓解过度平滑.
- 使用梯度增强决策树分类器用于DTI预测.
主要成果:
- 在基准数据集上,iGRLDTI与最先进的方法相比表现优越.
- 该方法成功地减轻了过度平滑,提高了特征表示的可区分性.
- 案例研究证实iGRLDTI能够识别具有可区分特征的新型DTI.
结论:
- 通过解决GNN的局限性,iGRLDTI为准确的DTI预测提供了有效的解决方案.
- 拟议的方法增强了毒品和目标代表的歧视力.
- 通过改进的DTI预测,iGRLDTI促进了高效和准确的药物发现.
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