基因表达数据挖掘通过混合双聚类改进GA和BA的基因表达数据挖掘
Zheng Wu1, Jian Wang2, Xue Wang2
1Department of Basic Education and Research, Changchun Sci-Tech University, Changchun, 130600, China. 100817@cstu.edu.cn.
Scientific reports
|September 26, 2025
概括
这项研究引入了一种新的双重聚类方法,使用增强的启发式算法来改善基因表达数据分析. 这种新方法在生物洞察方面实现了卓越的准确性和效率.
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
- 基因组学就是基因组学.
背景情况:
- 聚类基因表达数据对于理解生物过程和疾病至关重要.
- 基因表达数据中的高维度,复杂性和噪声构成了重大集群挑战.
- 传统的集群方法往往在准确性和效率方面不足.
研究的目的:
- 为基因表达数据分析开发一种先进的双重聚类方法.
- 增强启发式算法 (遗传和蝙蝠算法) 以提高优化.
- 克服生物信息学现有的集群方法的局限性.
主要方法:
- 将增强的遗传和蝙蝠算法集成到双重集群框架中.
- 改进遗传和蝙蝠算法中的优化过程.
- 评估方法对单峰和多峰函数的性能.
主要成果:
- 与其他启发式算法相比,增强的遗传和蝙蝠算法显示出更高的融合速度和准确性.
- 拟议的双重集群方法导致集群间距离更远,集群内部距离更近.
- 关键指标 (几何平均值0.99,轮系数1.0,戴维斯-博尔丁指数0.2,调整后的兰德指数0.92) 的表现优于其他双重集群方法.
结论:
- 新的双重集群方法有效地实现了高的集群间变异性和集群内部相似性.
- 这种方法显著提高了基因表达数据分析的效率和准确性.
- 该方法为推动生物信息学研究提供了强大的技术支持.
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