优化准确性和维度:为强大的癌症基因组学分类提供群集智能策略
Abrar Yaqoob1, Mushtaq Ahmad Mir2, R Vijaya Lakshmi3
1School of Advanced Science and Language, VIT Bhopal University, Kothri Kalan, 466114, India.
BioData mining
|November 19, 2025
概括
这项研究引入了一种混合虫优化器 (DBO) 和支持矢量机 (SVM) 模型,用于从基因表达数据中准确地分类癌症,从而提高精确医学.
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
- 机器学习在瘤学中的应用
背景情况:
- 高维基因表达数据在癌症分类方面存在挑战,包括数据冗余,噪音和过度拟合.
- 有效的特征选择对于提高癌症分类模型的准确性和可解释性至关重要.
研究的目的:
- 开发和评估一个新的混合框架,将虫优化器 (DBO) 结合起来,用于基因特征选择和支持矢量机器 (SVM) 用于癌症分类.
- 通过减少噪音和冗余来解决癌症分类中高维数据的局限性.
主要方法:
- 提出了一个混合DBO-SVM框架,利用DBO的自然灵感优化来选择信息基因子集.
- 使用带有辐射基函数 (RBF) 内核的支向量机来对所选特征进行分类.
- 该框架在各种公开可用的癌症基因表达数据集上得到了验证.
主要成果:
- DBO-SVM框架实现了高准确率:97.4-98.0%的二进制分类和84-88%的多类分类任务.
- 该模型表现出平衡的精度,回忆和F1分数,表明不同类别的性能强.
- 观察到计算成本显著降低,生物解释性得到改善.
结论:
- 拟议的DBO-SVM混合模型有效地提高了使用高维基基因表达数据的癌症分类准确性和效率.
- 这种方法显示出作为精准医学和生物医学数据分析的可靠工具的巨大潜力.
- 为特征选择集成DBO为复杂的生物数据挑战提供了一个有希望的策略.
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