惩罚回归和图模型中稳定性选择的自动校准
Automated calibration for stability selection in penalised regression and graphical models
AI总结：
提出一种通过最大化内部稳定性分数并纳入块结构的自动校准程序，适用于LASSO惩罚回归和图模型，在模拟与真实多组学数据中表现优异并揭示LRRN3跨组学作用。
摘要：
稳定性选择是在高维环境下识别与结果联合相关的稀疏特征集的一种极具吸引力的方法。我们提出了一种通过最大化内部稳定性分数并纳入先验已知块结构（如多组学）数据的自动校准程序。该方法适用于（LASSO）惩罚回归和图模型。模拟实验表明，我们的方法优于基于非稳定性的方法以及使用原始校准的稳定性选择方法。将多块图LASSO应用于挪威妇女与癌症研究的真实（表观遗传和转录组）数据，揭示了LRRN3在吸烟生物学反应中核心且可信的全新跨组学作用。所提方法已在R包sharp中实现。
英文摘要：
Stability selection represents an attractive approach to identify sparse sets of features jointly associated with an outcome in high-dimensional contexts. We introduce an automated calibration procedure via maximisation of an in-house stability score and accommodating a priori-known block structure (e.g. multi-OMIC) data. It applies to (LASSO) penalised regression and graphical models. Simulations show our approach outperforms non-stability-based and stability selection approaches using the original calibration. Application of multi-block graphical LASSO on real (epigenetic and transcriptomic) data from the Norwegian Women and Cancer study reveals a central/credible and novel cross-OMIC role of LRRN3 in the biological response to smoking. Proposed approaches were implemented in the R package sharp.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
