评估随机森林的自我复制性,以获得最佳的短期生物标志物签名发现
Ahmed Debit1,2,3, Christophe Poulet4,5,6, Claire Josse1,7
1Laboratory of Human Genetics, GIGA Institute, University of Liege (ULiege), Avenue Hippocrate 1/11, 4000 Liege, Belgium.
Briefings in bioinformatics
|July 11, 2025
概括
新的稳定性得分 (HRS和HSS) 通过评估分类算法的稳定性超出平均曲线下的面积 (AUC) 来改善生物标志物签名的发现. 这确保了可靠的诊断工具开发.
科学领域:
- 生物医学信息学是生物医学信息学.
- 机器学习在诊断中的应用.
- 发现生物标志物的发现.
背景情况:
- 生物标志物签名的发现对于临床诊断至关重要,特别是在有限的病理学知识的情况下.
- 较短的生物标志物签名是首选的,以实现诊断的成本效益.
- 生物标志物发现的算法选择通常依赖于平均曲线下面面积 (AUC),但AUC稳定性并不能保证.
研究的目的:
- 引入两个新的AUC衍生的超稳定性得分:超稳定性重新采样敏感 (HRS) 和超稳定性信号敏感 (HSS).
- 提出这些分数作为平均AUC的补充指标,以提高对分类算法选择的信心.
- 评估各种随机森林实现的性能和稳定性.
主要方法:
- 15个不同的随机森林实现的比较.
- 使用平均AUC性能对分类算法的评估.
- 介绍和应用拟议的HRS和HSS超稳定性得分.
主要成果:
- 对于所有数据集和分类任务来说,没有一个单一的随机森林实现是普遍最佳的.
- 算法的性能高度依赖于特定的数据和分类问题.
- 拟议的HRS和HSS得分为算法稳定性提供了宝贵的见解,超过了平均AUC.
结论:
- 选择随机森林实现生物标志物签名发现的选择应该取决于数据和任务.
- 仅仅平均AUC不足以选择可靠的分类算法.
- 预先测试平均AUC性能和AUC衍生的稳定性 (HRS,HSS) 对于可靠的诊断工具开发至关重要.
相关概念视频
01:24Bootstrapping
673
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
673
01:19Survival Tree
166
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
166
01:17Wald-Wolfowitz Runs Test I
744
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
The test works...
744