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基于种群特征的利用多方位多目标基因选择用于微阵列数据分类.

Min Li1, Rutun Cao1, Yangfan Zhao1

  • 1School of Information Engineering, Nanchang Institute of Technology, No. 289 Tianxiang Road, Nanchang, Jiangxi, PR China.

Computers in biology and medicine
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概括
此摘要是机器生成的。

这项研究介绍了MOMOGS-PCE,这是一种新的基因选择方法,它使用反向思维来利用局部最佳情况来改善癌症诊断. 这种方法有效地通过利用,而不是避免,人口融合问题来识别优越的基因子集.

关键词:
基因选择 基因选择微阵列数据的数据多个目标的多重目标.多重定位 多重定位反向思维是一种反向的思维.

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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:

  • 从微阵列数据中选择基因对于有效的癌症诊断和分类至关重要.
  • 群体智能算法经常面临过早的局部最佳的融合,阻碍了最佳的基因子集识别.

研究的目的:

  • 提出一种新的基因选择方法,MOMOGS-PCE,将局部最佳情况重新定义为机会而不是障碍.
  • 加强全球最佳基因子集的发现,用于癌症诊断.

主要方法:

  • 开发了MOMOGS-PCE,一种多目标基因选择方法,利用"反向思维"策略.
  • 实施了一种新的人口初始化策略,使用多个多样化的人口.
  • 采用了增强的NSGA-II算法来放大人口特征.
  • 引入了一种新的交换策略,用于人口间的特征转移.

主要成果:

  • 与其他六种多目标基因选择算法相比,MOMOGS-PCE在综合指标方面表现出显著的优势.
  • "逆向思维"方法成功地避免了局部最佳状态,并利用它们来发现优异的基因子集.
  • 验证了MOMOGS-PCE在确定癌症诊断最佳基因子集的有效性.

结论:

  • 拟议的MOMOGS-PCE方法通过利用局部最佳的方法在基因选择中提供了一个范式的转变.
  • 这种方法增强了对歧视性基因子集的识别,从而提高了癌症分类的准确性.
  • 在癌症研究中,MOMOGS-PCE为基因组数据分析提供了强大而有效的工具.