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对RNA表达数据的综合分析通过机器学习技术揭示了不同的癌症类型

Saad Awadh Alanazi¹, Nasser Alshammari¹, Maddalah Alruwaili²

¹Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf 72341, Saudi Arabia.

Saudi journal of biological sciences

|January 29, 2024

概括

机器学习有效地使用基因表达数据分类癌症亚型. 这种方法通过揭示BRCA和COAD等癌症的分子差异来增强个性化医疗.

科学领域:

在瘤学瘤学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

癌症的复杂性和异质性挑战了传统的基于遗传病理学的分类.
基因表达造型为分子分层和个性化治疗策略提供了一种强大的方法.

研究的目的:

应用机器学习来综合分析来自五种癌症类型 (BRCA,KIRC,COAD,LUAD,PRAD) 的RNA测序数据.
识别分子亚型并评估指导个性化治疗决策的潜力.

主要方法:

一个集成型机器学习工作流程,涉及数据集识别,规范化,特征选择,缩小维度,聚类 (k-means) 和分类.
分析BRCA,KIRC,COAD,LUAD和PRAD数据集中的RNA测序数据.

主要成果:

无监督机器学习基于基因表达模式识别了五个不同的集群,与已知的癌症类型有显著的相关性.
BRCA,KIRC,COAD,LUAD和PRAD显示了特定的集群分布,突出了不同的分子特征.
广泛的神经网络实现了高精度 (99.834%的验证,99.995%的测试) 的分类.

结论:

机器学习有效地使用转录基因特征划分癌症亚型,揭示内在异质性.
综合分析和分子亚型化增强对失调路径的理解,并支持个性化医学.
该计算框架为分类癌症基因表达和告知治疗策略提供了一个强大的方法.

关键词:

分类分类分类分类.集群集成是指集群集成.基因基因基因基因基因基因机器学习机器学习 RNA的表达,癌症的发生.瘤,诊断,诊断的时间

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