融合硼特与LIME，精准筛选基因表达分类关键基因
BOLIMES: Boruta and LIME optiMized fEature Selection for Gene Expression Classification
- 先用硼特算法剔除无关基因，再用LIME评估剩余基因局部重要性
- 通过迭代分类验证，找到使准确率最高的最优基因数
- 兼顾降维效果与模型可解释性，适合生物医学数据分析
基因表达分类是生物信息学中关键但具挑战性的任务，主要源于基因组数据的高维度和过拟合风险。为解决此问题，我们提出BOLIMES，一种新型特征选择算法，旨在通过系统性优化特征子集来提升基因表达分类性能。与仅依赖统计排序或分类器特定选择的传统方法不同，BOLIMES融合了硼特（Boruta）的稳健性与LIME的可解释性，确保仅保留最相关且有影响力的基因。首先，利用硼特通过对比每个特征与其随机化副本，过滤掉非信息基因，从而保留有价值的信息；随后，使用LIME基于分类器的局部重要性对剩余基因进行排序；最后，通过迭代分类评估确定最优特征子集，即在该数量下预测准确率最高。结合全面的特征选择与可解释性驱动的优化，该方法有效平衡了降维与高分类性能，为高维基因表达分析提供强大解决方案。
原文摘要 · Abstract (English)
Gene expression classification is a pivotal yet challenging task in bioinformatics, primarily due to the high dimensionality of genomic data and the risk of overfitting. To bridge this gap, we propose BOLIMES, a novel feature selection algorithm designed to enhance gene expression classification by systematically refining the feature subset. Unlike conventional methods that rely solely on statistical ranking or classifier-specific selection, we integrate the robustness of Boruta with the interpretability of LIME, ensuring that only the most relevant and influential genes are retained. BOLIMES first employs Boruta to filter out non-informative genes by comparing each feature against its randomized counterpart, thus preserving valuable information. It then uses LIME to rank the remaining genes based on their local importance to the classifier. Finally, an iterative classification evaluation determines the optimal feature subset by selecting the number of genes that maximizes predictive accuracy. By combining exhaustive feature selection with interpretability-driven refinement, our solution effectively balances dimensionality reduction with high classification performance, offering a powerful solution for high-dimensional gene expression analysis.
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