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开发一种机器学习框架,用于发现辐射生物标志物和预测吸收剂量

Björn Andersson¹, Britta Langen², Peidi Liu¹

¹Bioinformatics Core Facility, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.

Frontiers in oncology

|May 31, 2023

概括

机器学习 (ML) 在正常组织中识别新的辐射生物标志物,提高放射治疗和太空旅行中的速度和准确性. 这个框架有效地预测辐射剂量和组织类型,增强生物标志物发现.

科学领域:

辐射生物学辐射生物学
生物信息学是一种生物信息学.
机器学习机器学习

背景情况:

分子辐射生物标志物对于放射治疗,风险评估和太空旅行至关重要.
传统的生物标志物查是缓慢的,容易产生人类偏见.
机器学习 (ML) 为生物标志物识别提供了更好的灵敏度,特异性和速度.

研究的目的:

开发一个资源高效的ML框架,用于在正常组织中发现辐射生物标志物.
确定组织特定的生物标记面板,用于预测辐射剂量.

主要方法:

使用了一组转录基因数据集 (GSE44762) 来研究小鼠脏组织.
在R caret包中使用的ML模型用于特征选择和分析.
使用主要成分分析 (PCA) 和剂量回归评估生物标志物的性能.

主要成果:

与传统方法相比,caret框架显著减少了处理时间.
k-Nearest Neighbor (KNN) 证明了最佳的性能,识别了诸如*Cdkn1a*, *Gria3*, *Mdm2*, *Plk2*, *Brf2*, *Ccng1*和 *Ddit4l*之类的关键基因.
已识别的生物标志物面板准确地分类了辐射剂量组和组织,在剂量预测中具有很高的相关性 (R2=0.97-0.99).

结论:

卡雷特框架为辐射生物标志物发现提供了一个资源高效的工具.
发现了新的mRNA生物标志物 (*Brf2*, *Ddit4l*, *Gria3*) 具有潜在的剂量和组织特异性辐射反应.
建议使用更大的数据集进行进一步验证,以提高准确性,特别是在较低的辐射剂量下.

关键词:

吸收的剂量吸收的剂量生物标志物生物标志物卡雷特 (caret) 是一辆汽车.电离辐射辐射的电离辐射.kNN (k最近的邻居)放射性核化物中的放射性核化物.转录组学转录组学是指转录组学.

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