混合黑寡妇优化与代的贪算法用于基因选择问题
Mohammed Alweshah1, Yasmeen Aldabbas1, Bilal Abu-Salih2
1Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan.
Heliyon
|October 9, 2023
概括
这项研究引入了一种新的混合黑寡妇优化 (BWO-IG) 算法,用于在微阵列数据中高效的基因选择. 与传统方法相比,BWO-IG显著提高了准确性,并减少了基因数量.
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:
- 基因选择 (GS) 对于从DNA微阵列中减少高维基因表达数据集的复杂性至关重要.
- 微阵列数据分析面临的挑战是由于固有的数据不平衡和样本和基因数量之间的差异.
- 现有的GS方法可能是计算密集型,可能无法有效处理数据复杂性.
研究的目的:
- 在微阵列基因表达数据中开发一种简化和计算有效的属性选择方法.
- 通过改进优化算法的本地搜索能力来提高基因选择的效率.
- 通过现有方法验证新型混合算法的性能.
主要方法:
- 使用黑寡妇优化算法 (BWO) 进行基因选择.
- 开发并测试了两个BWO方法:一个不变的BWO和一个混合的BWO与代贪算法 (BWO-IG).
- 经验验证了使用九个基准微阵列基因表达数据集的BWO-IG方法,并将其与五种现代包装特征选择 (FS) 方法进行了比较.
主要成果:
- 混合的BWO-IG技术在传统的BWO算法上表现出卓越的性能.
- BWO-IG显示增强了本地搜索效率,从而更可靠地识别了相关基因.
- 对比分析显示,BWO-IG在减少选定基因数量的优势,同时实现了高分类准确性 (平均94.426%).
结论:
- 在微阵列数据分析中,BWO-IG算法为基因选择提供了计算有效和可靠的解决方案.
- 将BWO与Iterated Greedy混合,显著提高了基因修剪和分类准确度.
- BWO-IG的性能优于现有的包装FS方法,使其成为生物信息学研究的一个有前途的工具.
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