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评估核反应优化 (NRO) 算法用于癌症分类中的基因选择.

Shahad Alkamli1, Hala Alshamlan1

  • 1Department of Information Technology, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia.

Diagnostics (Basel, Switzerland)
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概括
此摘要是机器生成的。

核反应优化 (NRO) 从高维癌症微阵列数据中有效地选择信息基因. 这种方法对癌症分类有希望,尽管基因子集大小可以在没有进一步减少的情况下很高.

关键词:
生物信息学是一种生物信息学.癌症分类 癌症分类 癌症分类基因选择 基因选择微型阵列数据数据核反应优化 (NRO) 是指核反应的优化.优化算法优化算法

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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:

  • 从微阵列数据进行癌症分类是具有挑战性的,因为它具有很高的维度.
  • 有效的基因选择对于精确的癌症亚型和分析至关重要.
  • 需要先进的优化技术来处理复杂的基因组数据集.

研究的目的:

  • 引入和评估核反应优化 (NRO) 用于癌症微阵列数据集中的基因选择.
  • 评估NRO的业绩作为一个独立的方法,而没有事先减少维度.
  • 将NRO的有效性与癌症分类的其他优化算法进行比较.

主要方法:

  • 受核过程启发的核反应优化 (NRO) 被应用于六个癌症微阵列数据集.
  • 使用支持向量机 (SVM) 和k-最近邻居 (k-NN) 分类器来评估选定的基因子集.
  • 在强大的分类准确性评估中采用了Leave-One-Out交叉验证 (LOOCV).

主要成果:

  • NRO表现出高的分类准确性,特别是与SVM结合使用时.
  • 该算法在精选的数据集上显示了与HHO,ABC,PSO和FFA等最先进的方法相比的竞争性性能.
  • 由于缺乏额外的特征减少步骤,所选基因的数量相对较高.

结论:

  • 核反应优化 (NRO) 是癌症分类中基因选择的可行方法.
  • 通过将NRO与混合模型和特征减少技术集成,可以实现进一步的改进.
  • 未来的研究应该专注于优化基因子集大小,以提高效率和准确性.