用高质量的负样本和基于网络的深度学习框架预测药物向相互作用
IEEE journal of biomedical and health informatics
|January 16, 2024
概括
这项研究引入了一种新的方法,通过改进负样本选择和整合网络和生物数据来预测药物向相互作用 (DTI). 这种方法提高了计算药物发现的准确性.
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.
- 药物发现 药物发现
背景情况:
- 准确的药物向相互作用 (DTI) 识别对于有效的药物发现至关重要.
- 计算方法加速DTI预测,与实验相比,减少时间和成本.
- 目前用于DTI预测的现有机器学习方法的样本质量不佳,数据集成效率低下.
研究的目的:
- 为了应对在DTI预测中高质量的负样本选择的挑战.
- 为准确的DTI预测开发一个先进的计算框架.
- 整合各种数据源,包括网络拓和生物注释,以提高预测性能.
主要方法:
- 基于复杂网络理论开发了一种新的负样本选择策略,以减轻未标记数据中的偏差.
- 提出了一个新的DTI预测框架,HNetPa-DTI,将药物-蛋白质-疾病网络拓与蛋白质基因本体学 (GO) 和途径注释集成在一起.
- 异质图神经网络被用来从异质网络中提取拓特征,而图神经网络则通过各种网络结构处理GO和路径信息.
主要成果:
- 拟议的负样本选择方法有效地解决了随机选择中存在的偏见.
- 与现有的基线方法相比,HNetPa-DTI在四个不同的预测任务中表现出优异的性能.
- 不同质网络拓和蛋白质注释数据的整合显著提高了DTI预测的准确性.
结论:
- 开发的负样本选择方法提高了计算DTI预测的可靠性.
- 通过有效利用多源信息,HNetPa-DTI在DTI预测方面取得了重大进展.
- 这项研究提供了一个强大的计算框架,可以加速在药物发现管道中识别潜在的候选药物.
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