Biomarker discovery in microarray gene expression data with Gaussian processes
Biomarker discovery in microarray gene expression data with Gaussian processes
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DOI：
10.1093/bioinformatics/bti526
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发表时间：
2005-08-15
期刊：
影响因子：
5.8
通讯作者：
Wild, DL
中科院分区：
文献类型：
--
作者：
Chu, W;Ghahramani, Z;Wild, DL
动机：在临床实践中，病理表型通常用有序尺度而不是二进制标记，例如用于肿瘤细胞分化的Gleason分级系统。然而，在微阵列分析的文献中，这些序数标签很少以原则性的方式处理。本文描述了一种基于高斯过程的基因选择算法，以发现与有序临床表型相关的一致基因表达模式。自动相关性确定的技术被施加到表示的基因的显着性水平的贝叶斯推理framework.Results：所提出的算法的有用性证明了有序标签的基因表达签名与前列腺癌数据的Gleason评分。我们的研究结果表明，最初可能开发的诊断或预后应用的多基因标记物也可作为一种调查工具，以揭示特定的分子和细胞事件与肿瘤生理学特征之间的关联。我们的算法也可以应用到微阵列数据与二进制标签的结果与文献中的其他方法。
Motivation: In clinical practice, pathological phenotypes are often labelled with ordinal scales rather than binary, e.g. the Gleason grading system for tumour cell differentiation. However, in the literature of microarray analysis, these ordinal labels have been rarely treated in a principled way. This paper describes a gene selection algorithm based on Gaussian processes to discover consistent gene expression patterns associated with ordinal clinical phenotypes. The technique of automatic relevance determination is applied to represent the significance level of the genes in a Bayesian inference framework.Results: The usefulness of the proposed algorithm for ordinal labels is demonstrated by the gene expression signature associated with the Gleason score for prostate cancer data. Our results demonstrate how multi-gene markers that may be initially developed with a diagnostic or prognostic application in mind are also useful as an investigative tool to reveal associations between specific molecular and cellular events and features of tumour physiology. Our algorithm can also be applied to microarray data with binary labels with results comparable to other methods in the literature.