发表机构
Cedars-Sinai Health Sciences University（西达赛奈健康科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究重构优化扩展scikit-rebate包，对比新型Relief类算法在基因组模拟数据中的表现，发现MultiSWRFDB等算法可有效保留主效应与交互作用，运行时间显著缩短。
AI 中文摘要
作为高维生物医学数据建模的前置步骤，可靠的特征选择可降低计算开销、提升建模性能并生成更简单、更具可解释性的模型。然而，大多数过滤式特征选择方法难以检测特征交互，而包装式或嵌入式特征选择方法计算成本高昂。基于Relief的算法（RBAs）是一类过滤式方法，对特征交互敏感，同时可缓解上述其他限制。本研究：（1）利用现有及新提出的RBA变体，对scikit-rebate Python包进行重构、优化与扩展；（2）在多样化的基因组模拟数据中开展严格的RBA基准比较。我们将scikit-rebate扩展为包含SWRF*、mu-Relief及5种新型RBA变体，这些变体采用替代的邻居选择与特征评分策略。所有RBAs均接受评估，以比较其在样本量、特征数量、遗传率及潜在关联类型（如主效应与交互作用）各异的模拟基因组数据集上的预测特征排序与运行时间。除mu-Relief外，所有RBAs均能有效检测噪声数据中的双向交互。采用“far”评分的RBAs在检测双向交互方面表现最佳，其中MultiSWRFDB*性能最优，但对主效应的敏感性低得多。SWRF、MultiSWRF、MultiSURF及MultiSWRFDB在主效应与双向交互数据集上均表现出色，当同时考虑三向交互时，MultiSWRFDB性能最佳。scikit-rebate的重构使RBA运行时间减少了10至35倍。新引入的RBAs属于性能最强的算法之列，通过稳健保留主效应与双向上位性交互，这些算法为下游建模保留了预测信号。
英文摘要
As a precursor to high-dimensional biomedical data modeling, reliable feature selection can reduce computational expense, improve modeling performance, and yield simpler, more interpretable models. However, most filter-based feature selection methods struggle to detect feature interactions, while wrapper or embedded feature selection methods are computationally expensive. Relief-based algorithms (RBAs) are filter methods that are sensitive to feature interactions while mitigating these other limitations. This study (1) refactors, optimizes, and expands the scikit-rebate Python package with existing and newly proposed RBA variants and (2) conducts rigorous RBA benchmark comparisons across diverse genomic simulations. We expand scikit-rebate to include SWRF*, mu-Relief, and 5 novel RBA variants implementing alternative strategies for neighbor selection and feature scoring. All RBAs were evaluated to compare predictive feature ranking and runtime across simulated genomic datasets varying in sample size, number of features, heritability, and underlying association type (e.g. main effects and interactions). All RBAs, except mu-Relief, were proficient in detecting 2-way interactions in noisy data. RBAs utilizing 'far' scoring were best at detecting 2-way interactions - with MultiSWRFDB* top-performing - but were far less sensitive to main effects. SWRF, MultiSWRF, MultiSURF, and MultiSWRFDB yielded top performance across main effect and 2-way interaction datasets with MultiSWRFDB performing best when also considering 3-way interactions. Refactoring of scikit-rebate resulted in 10 to 35-fold reductions in RBA runtimes. The newly introduced RBAs were among the strongest performing, and by robustly retaining both main effects and 2-way epistatic interactions, these algorithms preserve predictive signals for downstream modeling.
Comments18 pages, 6 figures, 2 tables, submitted for journal review
