MEvA-X:一种混合多目标进化工具,使用XGBoost分类器在生物医学数据集上发现生物标志物
Konstantinos Panagiotopoulos1, Aigli Korfiati2, Konstantinos Theofilatos2,3
1PolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Turin, 10129, Italy.
Bioinformatics (Oxford, England)
|June 16, 2023
概括
MEvA-X是一种新的工具,通过结合进化算法和XGBoost分类来增强生物标志物发现. 它有效地处理类不平衡和多重目标,改善特征选择和模型简单性,用于精密医学应用.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 生物标志物发现对于精准医学,疾病预后和药物发现至关重要.
- 挑战包括样本与特征比率低以及数据集中的类不平衡.
- 像XGBoost这样的现有方法与多目标优化和类不平衡作斗争.
研究的目的:
- 介绍MEvA-X,用于特征选择和分类的混合组合.
- 解决当前处理阶级失衡和多重目标的方法的局限性.
- 优化 XGBoost 的超参数并同时执行功能选择.
主要方法:
- 开发了MEvA-X,集成基于利基的多目标进化算法 (EA) 与XGBoost.
- 利用多目标EA进行超参数优化和特征选择.
- 确定了帕雷托最佳解决方案,平衡了分类和模型简单性.
主要成果:
- MEvA-X的性能超过了对omics和临床数据集的最先进方法.
- 实现了平衡的类别分类,并生成了低复杂性的模型.
- 确定了非冗余生物标志物,包括用于精密营养的潜在血液循环标志物.
结论:
- MEvA-X为生物信息学中的生物标志物发现提供了一个强大的方法.
- 该工具有效地解决了阶级不平衡和多目标优化挑战.
- 在识别相关生物标志物的证明有用性,用于应用程序,如精密营养.
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