以集成方式穷尽利用自然启发计算进行癌症筛查
Exhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble Manner
AI总结：
本文提出 EODE 框架，融合灰狼优化特征选择、随机注入多样性增强与子集模型协同优化，在35个基因表达数据集上提升癌症筛查准确率。
摘要：
准确筛查癌症类型对于有效的癌症检测和精确的治疗选择至关重要。然而，基因表达谱与肿瘤之间的关联往往局限于少数生物标志物基因。尽管采用自然启发算法的计算方法已在选择预测性基因方面展现出前景，但现有技术受到搜索效率低下以及在多样化数据集上泛化能力不足的限制。本研究提出了一个名为 Evolutionary Optimized Diverse Ensemble Learning（EODE，进化优化多样化集成学习）的框架，以改进基于基因表达数据进行癌症分类的集成学习。EODE 方法结合了用于选择性特征空间降维的智能灰狼优化算法、用于增强集成多样性的引导随机注入建模，以及用于协同分类器组合的子集模型优化。研究在涵盖多种癌症类型的35个基因表达基准数据集上开展了大量实验。结果表明，与单个模型和传统聚合模型相比，EODE 取得了显著提升的筛查准确率。对先进特征选择、定向专门化建模和协作分类器集成的综合优化，有助于应对当前自然启发方法中的关键挑战。这为利用基因表达生物标志物进行稳健且泛化的集成学习提供了有效框架。具体而言，我们已在 Github 上公开 EODE 源代码：https://github.com/wangxb96/EODE。
英文摘要：
Accurate screening of cancer types is crucial for effective cancer detection and precise treatment selection. However, the association between gene expression profiles and tumors is often limited to a small number of biomarker genes. While computational methods using nature-inspired algorithms have shown promise in selecting predictive genes, existing techniques are limited by inefficient search and poor generalization across diverse datasets. This study presents a framework termed Evolutionary Optimized Diverse Ensemble Learning (EODE) to improve ensemble learning for cancer classification from gene expression data. The EODE methodology combines an intelligent grey wolf optimization algorithm for selective feature space reduction, guided random injection modeling for ensemble diversity enhancement, and subset model optimization for synergistic classifier combinations. Extensive experiments were conducted across 35 gene expression benchmark datasets encompassing varied cancer types. Results demonstrated that EODE obtained significantly improved screening accuracy over individual and conventionally aggregated models. The integrated optimization of advanced feature selection, directed specialized modeling, and cooperative classifier ensembles helps address key challenges in current nature-inspired approaches. This provides an effective framework for robust and generalized ensemble learning with gene expression biomarkers. Specifically, we have opened EODE source code on Github at https://github.com/wangxb96/EODE.
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。
