分析DNA甲基化阵列数据集的方法:一篇综述

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Updated: Jun 24, 2025

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Karishma Sahoo¹, Vino Sundararajan¹

¹Integrative Multiomics Lab, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India.

Computational and structural biotechnology journal

|June 7, 2024

概括

本综述探讨了用于DNA甲基化分析的计算工具,这对于识别疾病生物标志物至关重要. 它强调了整合基因表达和甲基化数据,以提高诊断准确性和预后预测.

科学领域:

基因组学就是基因组学.
表观遗传学在表观遗传学中,表观遗传学是指表观遗传学.
生物信息学是一种生物信息学.

背景情况:

了解基因表达和表观遗传修饰对于疾病病理学至关重要.
对DNA甲基化概况,特别是差异甲基化区域 (DMRs/DMGs),是生物标志物发现的关键.

研究的目的:

审查基于微阵列的DNA甲基化数据分析的计算工具和算法.
为分析患病基因组的甲基化数据所面临的挑战和趋势提供路线图.

主要方法:

对当前用于DNA甲基化分析的计算工具和算法的调查.
专注于用于诊断/预后CpG部位提取的方法.
探索基因表达和甲基化数据集的综合分析.

主要成果:

确定CpG部位提取用于诊断和预后的关键概念.
讨论方法框架,算法和管道.
强调机器学习,神经网络和用于诊断工作流程的数据挖掘.

结论:

整合基因表达和甲基化数据可以增强生物标记物识别.
先进的计算方法提高了诊断的准确性,精度和稳定性.
分子亚型化有助于使用甲基化数据进行疾病分类.

关键词:

生物标记物识别方法集群集成是指集群集成.在DMR分析中,分析了DMR.甲基化细分的细分方法分子子类型的分类.预测模型的预测模型.

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