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基于微RNA表达的癌症类型的分类,使用混合辐射基函数和粒子群优化算法

Masoumeh Soleimani¹, Aryan Harooni², Nasim Erfani³

¹Department of Mathematics and Statistical Sciences, Clemson University, Clemson, South Carolina, USA.

Microscopy research and technique

|January 17, 2024

概括

这项研究引入了一种使用microRNA表达数据进行癌症类型分类的改进方法. 混合辐射基函数和粒子群优化算法提高了识别癌症类型的准确性和可靠性.

科学领域:

生物技术是生物技术.
生物信息学是一种生物信息学.
基因组学就是基因组学.

背景情况:

微RNAs (miRNAs) 是关键的基因表达的调节者,涉及到各种癌症.
从miRNA表达数据准确地分类癌症类型,对于有效的诊断和治疗至关重要.
目前的方法在准确性和计算效率方面面临挑战.

研究的目的:

利用miRNA表达数据开发一种改进的计算方法,用于准确的癌症类型分类.
通过优化功能选择来提高分类准确度和降低计算负载.

主要方法:

采用了一种混合方法,将辐射基函数 (RBF) 神经网络与粒子群优化 (PSO) 结合起来.
使用PSO进行了高效的特征选择,以确定最相关的miRNA生物标志物.
数据预处理和规范化是在两个不同的miRNA表达数据集上进行的.

主要成果:

拟议的方法在两个数据集上实现了高分类准确率95%和91%.
获得了93%的平均精度,与现有方法相比,表现出优越的性能.
实际上,PSO有效地减少了功能集,从而提高了计算效率.

结论:

混合RBF-PSO方法为基于miRNA表达的癌症类型分类提供了强大的和准确的方法.
这种技术在推进癌症诊断和个性化医疗方面具有重大潜力.
通过PSO选择最小的特征,与RBF分类相结合,提高了可靠性和效率.

关键词:

癌症癌症癌症癌症癌症危害健康的风险这是一个微型RNA.粒子群集优化粒子群集优化辐射基础功能神经网络神经网络

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