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Bayesian approach to transforming public gene expression repositories into disease diagnosis databases

机译：贝叶斯方法将公共基因表达库转化为疾病诊断数据库

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摘要

The rapid accumulation of gene expression data has offered unprecedented opportunities to study human diseases. The National Center for Biotechnology Information Gene Expression Omnibus is currently the largest database that systematically documents the genome-wide molecular basis of diseases. However, thus far, this resource has been far from fully utilized. This paper describes the first study to transform public gene expression repositories into an automated disease diagnosis database. Particularly, we have developed a systematic framework, including a two-stage Bayesian learning approach, to achieve the diagnosis of one or multiple diseases for a query expression profile along a hierarchical disease taxonomy. Our approach, including standardizing cross-platform gene expression data and heterogeneous disease annotations, allows analyzing both sources of information in a unified probabilistic system. A high level of overall diagnostic accuracy was shown by cross validation. It was also demonstrated that the power of our method can increase significantly with the continued growth of public gene expression repositories. Finally, we showed how our disease diagnosis system can be used to characterize complex phenotypes and to construct a disease-drug connectivity map.
机译：基因表达数据的迅速积累为研究人类疾病提供了前所未有的机会。美国国家生物技术信息中心的基因表达综合中心是目前最大的数据库，能够系统地记录疾病的全基因组分子基础。但是，到目前为止，这种资源还没有得到充分利用。本文介绍了将公共基因表达库转化为疾病自动诊断数据库的第一项研究。特别是，我们已经开发了一个系统框架，包括两阶段贝叶斯学习方法，以实现沿着疾病分类法对查询表达谱进行一种或多种疾病的诊断。我们的方法包括标准化跨平台基因表达数据和异质性疾病注释，可在统一的概率系统中分析这两种信息源。交叉验证显示出较高的整体诊断准确性。还证明了，随着公共基因表达库的持续增长，我们方法的功能可以显着增加。最后，我们展示了如何将我们的疾病诊断系统用于表征复杂表型并构建疾病-药物连接图。

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    Haiyan Huang; Chun-Chi Liu; Xianghong Jasmine Zhou;

  • 作者单位

    Department of Statistics, University of California, Berkeley, CA 94720;

    Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089 Institute of Genomics and Bioinformatics, National Chung Hsing University, Taiwan;

    Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089;

  • 收录信息 美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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  • 正文语种 eng
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