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关键基因选择和癌症表型分类的综合方法:改善诊断和预测

Md Matiur Rahaman¹, Bandhan Sarker², Muhammad Habibulla Alamin³

¹Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Haining, 314400, PR China; Department of Statistics, Faculty of Science, Gopalganj Science and Technology University, Gopalganj, 8100, Bangladesh.

Computers in biology and medicine

|July 6, 2025

概括

这项研究引入了一种混合模型,将统计和机器学习方法结合起来,以改进癌症表型的分类. 该方法识别了关键的基因和调节器,增强了癌症研究的诊断和预后工具.

科学领域:

计算生物学和生物信息学
癌症研究和精准医学.
统计建模和机器学习.

背景情况:

准确的癌症表型分类对于诊断,预后和治疗至关重要.
现有的特征选择和分类方法可以改进,以获得更好的准确性和生物洞察力.

研究的目的:

开发和验证一个混合模型,整合统计和机器学习算法,用于增强癌症表型分类.
为了确定关键的基因和监管网络与癌症的诊断和预后意义.

主要方法:

使用五种统计测试 (LIMMA,SAM,ANOVA,KW-测试,t-测试) 进行初始特征识别.
使用各种机器学习分类器 (LDA,LR,NB,GPC,KNN,ANN,SVM,RF) 来进行表型分类.
在八个微阵列基因表达数据集上验证了模型,并进行了网络分析 (Gene-TF,Gene-miRNA).

主要成果:

混合模型表现出与现有基准相比的持续改进.
随机森林 (RF) 在二进制分类中表现出色,而带有辐射内核 (SVM-r) 的支持矢量机在多类设置中表现最好.
通过路径丰富验证,确定了13个关键基因和关键转录因子 (TFs) 和具有显著诊断和预后价值的微RNA (miRNAs).

结论:

拟议的混合方法有效地简化了特征选择,以准确地分类癌症表型.
鉴定的基因和调控网络为癌症诊断和预测提供了宝贵的生物学见解和潜在的生物标志物.
这项工作为开发更精确,更有效的癌症诊断和预后工具奠定了基础.

关键词:

癌症的分类癌症的分类功能选择功能选择基因表达数据基因表达数据机器学习是机器学习.微阵列的微阵列

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