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生物标志物检测使用纠正的国产程度在混合社交网络的特征选择,以提高分类器的性能

Hatice Yağmur Zengin¹, Erdem Karabulut²

¹Department of Biostatistics, Hacettepe University Faculty of Medicine, Sıhhiye, 06230, Ankara, Türkiye. yagmurzengin@hacettepe.edu.tr.

BMC bioinformatics

|October 31, 2023

概括

这项研究引入了一种混合特征选择方法,使用社交网络分析和一个新的指标,纠正的家庭程度,以改善癌症诊断. 该方法提高了对高维数据的分类性能和生物标志物识别.

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
机器学习机器学习

背景情况:

在癌症研究中常见的高维数据,需要有效的维度缩小以改进分类.
特征选择旨在识别信息基因/生物标志物,以准确诊断癌症.
现有的特征选择混合方法经常面临社会网络分析集成的挑战.

研究的目的:

开发和评估一种新的混合特征选择方法,用于高维的癌症数据.
通过集成的机器学习和社交网络分析,提高支持矢量机器分类器的性能.
引入和验证一个新的网络指标,即纠正的国产程度.

主要方法:

在R环境中实施了混合特征选择方法.
该方法将多个机器学习算法与社交网络分析集成在一起.
一个拟议的网络度量,纠正的家庭程度,被用于特征排名.

主要成果:

与使用整个特征集相比,提出的方法显著提高了分类器的性能.
通过ROC曲线下的面积来衡量分类准确度,超过了现有的文献方法.
通过考虑社区间的联系,修正后的国内性程度提供了更准确的特征排名.

结论:

纠正的国内度是排名特征的有价值指标,特别是那些外部连接有限的特征.
在混合方法中研究各种组合对于优化特定微阵列数据集的解决方案至关重要.
开发的混合方法为提高癌症研究中的生物标志物发现和诊断精度提供了一个有希望的策略.

关键词:

生物标志物生物标志物分类分类分类分类.缩小尺寸的缩小方式基因组学就是基因组学.在R基因组学.社交网络功能选择功能选择

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