优化癌症诊断:基因操作员和Sinh Cosh Optimizer的混合方法,用于瘤识别和特征基因选择
Marwa M Emam1, Essam H Houssein1, Nagwan Abdel Samee2
1Faculty of Computers and Information, Minia University, Minia, Egypt.
Computers in biology and medicine
|August 11, 2024
概括
这项研究引入了一种混合优化算法 (SCHO-GO),用于癌症微阵列数据中的基因选择. 这种新方法显著提高了使用支向量机分类器的瘤识别准确度.
科学领域:
- 生物信息学是一种生物信息学.
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 从基因表达数据中识别瘤至关重要,但由于高维度而具有挑战性.
- 有效的特征选择对于微阵列分析中准确的分类至关重要.
- 现有的方法与广的搜索空间作斗争,需要先进的混合方法.
研究的目的:
- 开发一种新的包装特征选择方法,用于在微阵列数据中增强瘤识别.
- 引入一个混合优化算法,SCHO-GO,结合基因运算符和Sinh Cosh优化器.
- 提高分类准确度,减少用于模式识别的数据维度.
主要方法:
- 开发了一种混合优化算法SCHO-GO,用于封装特征选择.
- 该SCHO-GO算法将一个遗传操作员与Sinh Cosh优化器 (SCHO) 集成在一起.
- 提出的方法是使用基因表达数据集上的SVM分类来评估的.
主要成果:
- 在基准函数上,SCHO-GO算法与七个元启发式算法相比,表现优越.
- 该SCHO-GO-SVM模型在基因表达癌症RNA-Seq数据集上实现了99.01%的准确性.
- 该方法有效地减少了维度,并提高了癌症分类的准确性.
结论:
- 在复杂的微阵列数据中,SCHO-GO算法为瘤识别和分类提供了强大而精确的方法.
- 这种混合优化技术显示了在癌症研究中推动生物信息学和人工智能的前景.
- 该SCHO-GO-SVM模型为基因表达数据分析的准确性提供了显著的改进.
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