一个低成本的机器学习框架,用于预测基于多个特征的融合和参数自调策略的药物相互作用
Zexiao Liang1, Canxin Lin2, Guoliang Tan3
1School of Integrated Circuits, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China. jianzhong.li@gdut.edu.cn.
Physical chemistry chemical physics : PCCP
|February 2, 2024
概括
这项研究引入了一种新的基于多视图半监督图 (MVSG) 的框架,用于预测药物相互作用 (DDI). 与传统方法相比,MVSG提供了更高的准确性和效率,即使数据有限.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 多药疗法需要了解药物相互作用 (DDI),以便进行安全有效的治疗.
- 目前用于预测DDI的方法,包括监督和深度学习模型,都有局限性.
- 智能算法为预测潜在的DDI提供了一个有希望的途径.
研究的目的:
- 为DDI分析和预测提出一个新的基于多视图半监督图 (MVSG) 的框架.
- 通过整合多个功能而不需要广泛的培训来克服现有的DDI预测技术的局限性.
- 通过利用各种数据特征,提供DDI的全面判断.
主要方法:
- 开发了基于多视图半监督图形 (MVSG) 的框架.
- 集成多个DDI功能和功能,用于全面分析.
- 采用参数自调策略,根据特征贡献合并图形.
- 利用来自公共数据库的抗癌药物数据进行评估.
主要成果:
- MVSG框架通过整合多个功能,证明了准确的DDI预测.
- MVSG比传统的机器学习技术取得了更高的性能,尤其是在有限的标记数据下.
- 该框架不需要耗时的培训过程.
- 对抗癌症药物数据的验证 (904种药物,7730种DDI记录,19种相互作用类型) 证实了有效性.
结论:
- MVSG框架为DDI预测提供了一种有效和高效的方法.
- MVSG通过利用多视图功能和基于图形的学习来提高DDI分析的准确性.
- 该框架可用于识别用于多药疗法的潜在有价值的药物组合.
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