基因对方法在临床研究中的应用:推进精准医学。

IF 6.3 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Changchun Wu, Xueqin Xie, Xin Yang, Mengze Du, Hao Lin, Jian Huang
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引用次数: 0

摘要

高通量测序技术的快速发展彻底改变了生物医学研究,产生了大量的基因表达数据,这些数据对生物学发现和临床应用具有巨大的潜力。有效挖掘这些大规模、高维数据对于促进疾病检测、亚型分化和理解疾病进展的分子机制至关重要。然而,传统的单基因分析模式,测量单个基因的绝对表达水平,在临床实施中面临着严重的局限性。其中包括对批处理效果和依赖于平台的规范化需求的脆弱性。相比之下,新兴的分析基因对之间相对表达关系的方法显示出独特的优势。通过关注两个基因表达量的二元比较,这些方法在捕获生物稳定的相互作用模式的同时,本质上使实验差异正常化。在这篇综述中,我们系统地评估了基于基因对的分析框架。我们将11种计算方法分为两大类:基于表达值的方法量化差异表达模式,以及基于转录顺序关系的排名方法。为了将方法学发展与实际实施联系起来,我们建立了一个可重复的分析管道,包括特征选择、分类器构建和模型评估模块,使用来自肺结核研究的真实基准数据集。这些发现将基因对分析定位为挖掘高维组学数据的变革范例,对精确的生物标志物发现和疾病进展的机制研究具有直接意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applications of gene pair methods in clinical research: advancing precision medicine.

The rapid evolution of high-throughput sequencing technologies has revolutionized biomedical research, producing vast amounts of gene expression data that hold immense potential for biological discovery and clinical applications. Effectively mining these large-scale, high-dimensional data is crucial for facilitating disease detection, subtype differentiation, and understanding the molecular mechanisms underlying disease progression. However, the conventional paradigm of single-gene profiling, measuring absolute expression levels of individual genes, faces critical limitations in clinical implementation. These include vulnerability to batch effects and platform-dependent normalization requirements. In contrast, emerging approaches analyzing relative expression relationships between gene pairs demonstrate unique advantages. By focusing on binary comparisons of two genes' expression magnitudes, these methods inherently normalize experimental variations while capturing biologically stable interaction patterns. In this review, we systematically evaluate gene pair-based analytical frameworks. We classify eleven computational approaches into two fundamental categories: expression value-based methods quantifying differential expression patterns, and rank-based methods exploiting transcriptional ordering relationships. To bridge methodological development with practical implementation, we establish a reproducible analytical pipeline incorporating feature selection, classifier construction, and model evaluation modules using real-world benchmark datasets from pulmonary tuberculosis studies. These findings position gene pair analysis as a transformative paradigm for mining high-dimensional omics data, with direct implications for precision biomarker discovery and mechanistic studies of disease progression.

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来源期刊
CiteScore
6.30
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