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Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations.商业短寡核苷酸微阵列的性能评估以及噪声对跨平台相关性的影响。
BMC Genomics. 2004 Sep 2;5:61. doi: 10.1186/1471-2164-5-61.
3
Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data.基于时间序列基因表达数据的基因网络非线性建模的动态贝叶斯网络和非参数回归
Biosystems. 2004 Jul;75(1-3):57-65. doi: 10.1016/j.biosystems.2004.03.004.
4
Increased measurement accuracy for sequence-verified microarray probes.经序列验证的微阵列探针的测量精度提高。
Physiol Genomics. 2004 Aug 11;18(3):308-15. doi: 10.1152/physiolgenomics.00066.2004.
5
Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks.利用动态贝叶斯网络从微阵列实验推断基因调控相互作用的敏感性和特异性。
Bioinformatics. 2003 Nov 22;19(17):2271-82. doi: 10.1093/bioinformatics/btg313.
6
A novel strategy for microarray quality control using Bayesian networks.一种使用贝叶斯网络进行微阵列质量控制的新策略。
Bioinformatics. 2003 Nov 1;19(16):2031-8. doi: 10.1093/bioinformatics/btg275.
7
Evaluation of gene expression measurements from commercial microarray platforms.商业微阵列平台基因表达测量的评估。
Nucleic Acids Res. 2003 Oct 1;31(19):5676-84. doi: 10.1093/nar/gkg763.
8
A model-based analysis of microarray experimental error and normalisation.基于模型的微阵列实验误差与标准化分析。
Nucleic Acids Res. 2003 Aug 15;31(16):e96. doi: 10.1093/nar/gng097.
9
Congruence of tissue expression profiles from Gene Expression Atlas, SAGEmap and TissueInfo databases.来自基因表达图谱、SAGEmap和组织信息数据库的组织表达谱的一致性。
BMC Genomics. 2003 Jul 29;4(1):31. doi: 10.1186/1471-2164-4-31.
10
Reproducibility of gene expression across generations of Affymetrix microarrays.Affymetrix微阵列各代间基因表达的可重复性。
BMC Bioinformatics. 2003 Jun 25;4:27. doi: 10.1186/1471-2105-4-27.

使用贝叶斯网络对微阵列研究结果通过逆转录聚合酶链反应进行概率建模并提高验证的可能性。

Use of Bayesian networks to probabilistically model and improve the likelihood of validation of microarray findings by RT-PCR.

作者信息

English Sangeeta B, Shih Shou-Ching, Ramoni Marco F, Smith Lois E, Butte Atul J

机构信息

Stanford Center for Biomedical Informatics Research (BMIR), Stanford University School of Medicine, 251 Campus Drive, Stanford, CA 94305, USA.

出版信息

J Biomed Inform. 2009 Apr;42(2):287-95. doi: 10.1016/j.jbi.2008.08.009. Epub 2008 Aug 26.

DOI:10.1016/j.jbi.2008.08.009
PMID:18790084
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3962641/
Abstract

Though genome-wide technologies, such as microarrays, are widely used, data from these methods are considered noisy; there is still varied success in downstream biological validation. We report a method that increases the likelihood of successfully validating microarray findings using real time RT-PCR, including genes at low expression levels and with small differences. We use a Bayesian network to identify the most relevant sources of noise based on the successes and failures in validation for an initial set of selected genes, and then improve our subsequent selection of genes for validation based on eliminating these sources of noise. The network displays the significant sources of noise in an experiment, and scores the likelihood of validation for every gene. We show how the method can significantly increase validation success rates. In conclusion, in this study, we have successfully added a new automated step to determine the contributory sources of noise that determine successful or unsuccessful downstream biological validation.

摘要

尽管全基因组技术，如微阵列，被广泛应用，但这些方法得到的数据被认为存在噪声；在下游生物学验证中仍有不同程度的成功。我们报告了一种方法，该方法使用实时逆转录聚合酶链反应（real time RT-PCR）提高成功验证微阵列结果的可能性，包括低表达水平和差异较小的基因。我们使用贝叶斯网络根据一组初始选定基因验证的成功与失败来识别最相关的噪声源，然后基于消除这些噪声源来改进我们后续用于验证的基因选择。该网络展示了实验中显著的噪声源，并对每个基因的验证可能性进行评分。我们展示了该方法如何能显著提高验证成功率。总之，在本研究中，我们成功添加了一个新的自动化步骤来确定决定下游生物学验证成功或失败的噪声的促成来源。