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 Srivastava1, J Ramanujam1
1Louisiana State University.
Research square
|November 14, 2023
概括
我们开发了一种新的数据增强协议,以扩展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) 对于识别新药组合至关重要,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.
- 提高药物组合发现ML模型的精度和有效性.
主要方法:
- 用一种新的药物相似度指标来替代具有类似药理作用的化合物.
- AZ-DREAM挑战数据集从8,798个增加到600多万种药物组合.
主要成果:
- 在增强数据上训练的机器学习模型 (随机森林,梯度增强树) 与在原始数据上训练的模型相比,显示出更高的准确性.
- 数据增强协议成功生成了更大,更多样化的数据集.
结论:
- 拟议的协议提供了一种系统和公正的方法,用于生成大规模的药物组合数据集.
- 这种方法可以开发更精确的ML模型,以发现有效的抗癌药物组合.
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