增强药物组合数据集,以提高预测协同抗癌效应的机器学习模型的性能
Mengmeng Liu1, Gopal Srivastava2, J Ramanujam1,3
1Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
Scientific reports
|January 18, 2024
概括
这项研究引入了一种新的数据增强协议,以扩大抗癌药物协同效应数据集. 这种方法提高了机器学习模型的准确性,以发现有效的癌症药物组合.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
背景情况:
- 组合疗法对于提高癌症治疗效率和克服耐药性至关重要.
- 机器学习 (ML) 对于发现新药组合至关重要,但目前的数据集往往不足以用于高精度模型.
研究的目的:
- 开发一个数据增强协议,以不偏地扩展抗癌药物协同效应数据集.
- 提高ML模型的精度和有效性,以预测协同作用的药物组合.
主要方法:
- 开发了一个使用新药相似度指标的数据增强协议.
- 在现有药物组合中用类似分子替代化合物,以产生新的实例.
- 将AZ-DREAM挑战数据集从8,798个扩展到超过600万个组合.
主要成果:
- 在增强数据上训练的ML模型与在原始数据上训练的模型相比,始终表现出更高的准确性.
- 增强协议成功扩展了数据集,提供了更多样化和更大的药物组合池.
- 该方法在数据生成方面被证明是系统的和公正的.
结论:
- 拟议的数据增强协议使得开发更精确,更有效的ML模型能够用于抗癌药物发现.
- 这种系统的方法可以作为未来对新型组合疗法的研究的基础.
- 增强的数据集对于通过ML推进精密瘤学领域至关重要.
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