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)
|April 12, 2025
概括
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.
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.
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