用神经网络和XGBoost学习曲线来预测药物反应的多种模式的比较
Nikhil Branson1,2, Pedro R Cutillas3, Conrad Bessant1,2
1School of Biological and Behavioural Sciences, Queen Mary University of London, London E1 4NS, United Kingdom.
Bioinformatics advances
|January 29, 2024
概括
蛋白质组学和光蛋白质组学显示出对抗癌药物反应预测的前景,其表现可能超过转录组学 (RNA-seq). 学习曲线可以预测这些omics数据类型的更大数据集的未来性能改进.
科学领域:
- 生物化学 生化学
- 基因组学就是基因组学.
- 蛋白质组学是指蛋白质组学.
背景情况:
- 对抗癌症药物反应的预测对于个性化医学至关重要.
- 转录组形状是常用的,但是蛋白质组学和蛋白质组学提供了更直接的细胞过程洞察力.
- 缺乏对这些omics数据类型进行系统的比较,以预测药物反应.
研究的目的:
- 系统地比较转录组学,蛋白质组学和蛋白质组学对抗癌药物反应的预测性能.
- 用不同数据集大小的学习曲线来评估当前和预测未来的表现.
- 为了比较神经网络和XGBoost在不同omics数据中对药物反应预测的有效性.
主要方法:
- 利用学习曲线来评估预测性能作为数据集大小的函数.
- 使用神经网络和XGBoost模型,与基于规则的方法进行基准测试.
- 分析的数据集包括38个细胞系与所有三种奥米克类型和877个细胞系与蛋白质和RNA-seq.
主要成果:
- 蛋白组学在预测38个细胞系的药物反应方面比RNA-seq和蛋白组学略有优势.
- 在使用877个细胞系预测药物反应方面,RNA-seq略高于蛋白质组学.
- 学习曲线预测,对于具有较大的数据集的基蛋白质组学,平均平方误差下降15%.
结论:
- 蛋白质组和蛋白质组是转录组的可行的替代品,用于预测抗癌药物反应.
- 机器学习模型 (神经网络与XGBoost) 的选择取决于数据集大小.
- 预计未来的数据采集将进一步提高这些omics数据集的预测能力.
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