Relative expression analysis for molecular cancer diagnosis and prognosis.
Relative expression analysis for molecular cancer diagnosis and prognosis.
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DOI：
10.1177/153303461000900204
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发表时间：
2010-04
影响因子：
2.8
通讯作者：
Price ND
中科院分区：
文献类型：
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作者：
Eddy JA;Sung J;Geman D;Price ND
高通量技术产生的大量生物分子测量数据增加了对生物分析中计算工具的需求。这些工具可以通过准确分类表型、检测疾病的存在、区分癌症亚型、预测临床结果和表征疾病进展来增强我们对人类健康和遗传疾病（如癌症）的理解。在基因表达微阵列数据的情况下，标准的统计学习方法已被用于识别能够准确区分疾病表型的分类器。然而，这些数学预测规则通常非常复杂，并且它们缺乏提取潜在生物学意义或过渡到临床所需的便利性和简单性。在这篇综述中，我们调查了一个强大的计算方法来分析转录组微阵列数据，解决这些限制。相对表达分析（RXA）仅基于少量基因表达之间的相对排序。具体来说，我们提供了RXA的第一个也是最简单的例子，k-TSP分类器，它基于k对基因; k = 1的情况是TSP分类器。由于其简单和易于生物学解释，以及它们对数据标准化和参数拟合的不变性，这些分类器已被广泛应用于帮助广泛的人类癌症的分子诊断。我们回顾了几项研究，这些研究证明了疾病表型的准确分类（例如，癌症与正常），癌症亚类（例如，AML vs. ALL，GIST vs. LMS）、疾病结局（例如，转移、存活）和通过血液传播的白细胞测定的多种人类病理学。提出的研究表明，RXA-特别是TSP和k-TSP分类器-是一个有前途的新一类的计算方法，用于分析高通量数据，并有可能显着有助于分子癌症的诊断和预后。
The enormous amount of biomolecule measurement data generated from high-throughput technologies has brought an increased need for computational tools in biological analyses. Such tools can enhance our understanding of human health and genetic diseases, such as cancer, by accurately classifying phenotypes, detecting the presence of disease, discriminating among cancer sub-types, predicting clinical outcomes, and characterizing disease progression. In the case of gene expression microarray data, standard statistical learning methods have been used to identify classifiers that can accurately distinguish disease phenotypes. However, these mathematical prediction rules are often highly complex, and they lack the convenience and simplicity desired for extracting underlying biological meaning or transitioning into the clinic. In this review, we survey a powerful collection of computational methods for analyzing transcriptomic microarray data that address these limitations. Relative Expression Analysis (RXA) is based only on the relative orderings among the expressions of a small number of genes. Specifically, we provide a description of the first and simplest example of RXA, the k-TSP classifier, which is based on k pairs of genes; the case k = 1 is the TSP classifier. Given their simplicity and ease of biological interpretation, as well as their invariance to data normalization and parameter-fitting, these classifiers have been widely applied in aiding molecular diagnostics in a broad range of human cancers. We review several studies which demonstrate accurate classification of disease phenotypes (e.g., cancer vs. normal), cancer subclasses (e.g., AML vs. ALL, GIST vs. LMS), disease outcomes (e.g., metastasis, survival), and diverse human pathologies assayed through blood-borne leukocytes. The studies presented demonstrate that RXA—specifically the TSP and k-TSP classifiers—is a promising new class of computational methods for analyzing high-throughput data, and has the potential to significantly contribute to molecular cancer diagnosis and prognosis.
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影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
168.9
作者：
Michiels, S;Koscielny, S;Hill, C
通讯作者：
Hill, C
影响因子：
3
作者：
Lin X;Afsari B;Marchionni L;Cope L;Parmigiani G;Naiman D;Geman D
通讯作者：
Geman D
影响因子：
4.3
作者：
Lee E;Chuang HY;Kim JW;Ideker T;Lee D
通讯作者：
Lee D
影响因子：
9.9
作者：
通讯作者：
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