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推进基于法医的调查,包括粘液模具搜索,用于对高维基遗传数据的基因选择
Feng Qiu1, Ali Asghar Heidari2, Yi Chen3
1Institute of Big Data and Information Technology, Wenzhou University, Wenzhou, 325035, China.
Scientific reports
|April 13, 2024
概括
这项研究介绍了SMA_FBI,一种基于法医 (FBI) 的研究算法,用于基因选择 (GS). 它的二进制版本,BSMA_FBI,通过更少的特征实现了高精度,证明了遗传数据分析的潜力.
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:
- 来自先进测序的高维基遗传数据集需要高效的临床决策处理.
- 基因选择 (GS) 对于在遗传数据分析中减少维度和提高性能至关重要.
研究的目的:
- 提出一种改进的包装基因选择方法,使用一种增强的取证调查 (FBI) 算法.
- 将粘液模具算法 (SMA) 引入到FBI框架中,创建SMA_FBI算法.
主要方法:
- 通过将粘液模具算法的搜索机制集成到基于法医的调查中,开发了SMA_FBI.
- 将连续SMA_FBI优化器转换为二进制版本 (BSMA_FBI),使用基因选择的转移函数.
- 验证了CEC2017测试套件上的SMA_FBI和UCI存储库中的高维遗传数据上的BSMA_FBI.
主要成果:
- 在找到最佳解决方案,融合速度和稳定性方面,SMA_FBI超过了10个原始和10个最先进的算法.
- 在18个高维基遗传数据集上,BSMA_FBI在较少的选择特征上实现了高分类准确性.
- 在基因选择应用中,与其他8个二进制算法相比,表现出优异的性能.
结论:
- SMA_FBI是一个强大的优化工具,用于解决全球优化问题.
- 对于基因选择任务,BSMA_FBI是有效的,在遗传数据分析中提供高精度和特征减少.
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