一个基于矩阵分解的新型加权伪标签框架,用于预测药物不良反应
Junheng Chen1, Fangfang Han2,3,4, Mingxiu He1
1School of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, China.
BMC bioinformatics
|February 18, 2025
概括
预测药物不良反应 (ADR) 对公共健康至关重要. 这项研究引入了一种新的加权伪标签框架,使用矩阵分解来提高ADR预测的准确性,特别是在稀疏的数据中.
科学领域:
- 药物监督 药物监督 药物监督
- 计算机化药物安全研究
- 医疗保健中的机器学习
背景情况:
- 药物不良反应 (ADRs) 对全球的健康构成重大风险和经济负担.
- 准确预测ADR对于早期干预和患者安全至关重要.
- 目前使用药物不良反应矩阵预测ADR的方法存在数据稀疏性,限制机器学习性能.
研究的目的:
- 开发一种新的加权伪标签框架,以解决预测药物不良反应 (ADR) 关联的数据稀疏性.
- 通过整合挖掘的伪标记药物-ADR对来提高用于ADR预测的机器学习模型的分类性能.
- 通过一种新的加权方法,提高伪标签的质量并防止伪标签过度贴合.
主要方法:
- 集成多重加权矩阵因子化 (MF) 模型来挖掘潜在的未知药物-ADR对.
- 使用加权的伪标签策略,将新发现的药物-ADR对纳入训练集.
- 用伪标签数据微调MF模型,并采用新的伪标签权重方法来提高稳定性.
主要成果:
- 拟议的加权伪标签框架在预测SIDER数据库中稀疏数据的ADR方面显著超过了基线方法.
- 使用Area Under Precision-Recall和F1得分,特别是在越来越稀疏的场景中,表现出卓越的性能.
- 一个案例研究证实了该框架在现实世界ADR预测中的效率.
结论:
- 新的加权伪标签框架有效地减轻了药物ADR预测中的数据稀疏性挑战.
- 这种方法提高了预测药物不良反应的准确性和可靠性,有助于提高患者安全.
- 该方法在药物监测和药物安全监测方面显示出实际应用的前景.
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