TL;DR
This work proposes post-pretrained lasso selective inference (PPL-SI), a novel selective inference method designed to provide statistically valid p values for the pretrained lasso that reliably controls false discoveries and significantly improves the detection of biologically relevant features compared to traditional approaches.
Empirical evaluations on eight public real-world biomedical HDLSS gene-expression datasets demonstrate that GINet-DGC achieves competitive and stable performance against 17 baselines, and support the effectiveness of the proposed framework within the evaluated public biomedical HDLSS benchmark setting.
Eleven practical tips for reducing unintentional overfitting in supervised biomedical machine learning studies are presented and stress principled data splitting, domain-informed preprocessing, controlled model complexity, systematic tuning, comprehensive performance evaluation, and robustness analysis.
The survkl software is introduced, which enables the incorporation of external summary-level information with newly collected time-to-event data to support more robust and accurate predictions in survival analysis.
The Normalised Sensitivity Ratio (NSR) is introduced, a post-hoc, model-agnostic diagnostic for this question under a structured-shift regime: environments differ primarily in the mean of spurious features while the causal mechanism and causal marginals remain stable, as in multi-site clinical data or multi-batch genom...
We consider the problem of high-dimensional inference with the lasso estimator. Different methods including'double selection'techniques and multiple versions of the'debiased lasso'have been proposed for this task with noticeable success. However, most guarantees assume strong hypotheses on the underlying data process a...
The Bootstrap-Enhanced Regularization Method (BERM) is a robust approach for variable selection and coefficient estimation in complex biomedical datasets, achieving the highest overall balanced accuracy while maintaining competitive coefficient estimation performance across a range of simulated sparsity, noise, and dim...
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