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Robust biomarker identification for cancer diagnosis with ensemble feature selection methods.基于集成特征选择方法的癌症诊断稳健生物标志物识别。
Bioinformatics. 2010 Feb 1;26(3):392-8. doi: 10.1093/bioinformatics/btp630. Epub 2009 Nov 25.
2
Comparison of feature selection and classification combinations for cancer classification using microarray data.使用微阵列数据进行癌症分类时特征选择与分类组合的比较。
Int J Bioinform Res Appl. 2009;5(4):417-31. doi: 10.1504/IJBRA.2009.027515.
3
Intra-platform repeatability and inter-platform comparability of microRNA microarray technology.微小RNA微阵列技术的平台内重复性和平台间可比性
PLoS One. 2009;4(5):e5540. doi: 10.1371/journal.pone.0005540. Epub 2009 May 14.
4
Prediction of malignant breast lesions from MRI features: a comparison of artificial neural network and logistic regression techniques.基于MRI特征预测乳腺恶性病变:人工神经网络与逻辑回归技术的比较
Acad Radiol. 2009 Jul;16(7):842-51. doi: 10.1016/j.acra.2009.01.029. Epub 2009 May 5.
5
Array-based comparative genomic hybridization as a tool for analyzing the leukemia genome.基于阵列的比较基因组杂交技术作为分析白血病基因组的工具。
Methods Mol Biol. 2009;538:151-77. doi: 10.1007/978-1-59745-418-6_8.
6
Daily clinical practice of fresh tumour tissue freezing and gene expression profiling; logistics pilot study preceding the MINDACT trial.新鲜肿瘤组织冷冻及基因表达谱分析的日常临床实践;MINDACT试验之前的物流试点研究。
Eur J Cancer. 2009 May;45(7):1201-1208. doi: 10.1016/j.ejca.2009.01.004. Epub 2009 Feb 14.
7
Quality assessment and data analysis for microRNA expression arrays.微小RNA表达阵列的质量评估与数据分析
Nucleic Acids Res. 2009 Feb;37(2):e17. doi: 10.1093/nar/gkn932. Epub 2008 Dec 22.
8
Use of diagnostic accuracy as a metric for evaluating laboratory proficiency with microarray assays using mixed-tissue RNA reference samples.使用诊断准确性作为评估使用混合组织RNA参考样本的微阵列分析实验室熟练度的指标。
Pharmacogenomics. 2008 Nov;9(11):1753-63. doi: 10.2217/14622416.9.11.1753.
9
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification.基于微阵列的癌症分类中随机森林与支持向量机的全面比较
BMC Bioinformatics. 2008 Jul 22;9:319. doi: 10.1186/1471-2105-9-319.
10
Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study.基于基因表达的肺腺癌生存预测:一项多中心、盲法验证研究。
Nat Med. 2008 Aug;14(8):822-7. doi: 10.1038/nm.1790. Epub 2008 Jul 20.

影响临床应用中基因表达谱一致性的标准。

Standards affecting the consistency of gene expression arrays in clinical applications.

机构信息

Molecular Genomics Laboratory, H. Lee Moffitt Cancer Center and Research Institute, SRB2 12902 Magnolia Drive, Tampa, FL 33612, USA.

出版信息

Cancer Epidemiol Biomarkers Prev. 2010 Apr;19(4):1000-3. doi: 10.1158/1055-9965.EPI-10-0044. Epub 2010 Mar 23.

DOI:10.1158/1055-9965.EPI-10-0044
PMID:20332273
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2852479/
Abstract

The use of microarray technology to measure gene expression has created optimism for the feasibility of using molecular assessments of tumors routinely in the clinical management of cancer. Gene expression arrays have been pioneers in the development of standards; both for research use and now for clinical application. Some of the existing standards have been driven by the early perception that microarray technology was inconsistent and perhaps unreliable. More recent experimentation has shown that reproducible data can be achieved and clinical standards are beginning to emerge. For the transcriptional assessment of tumors, this means a system that correctly samples a tumor, isolates RNA and processes this for microarray analysis, evaluates the data, and communicates findings in a consistent and timely fashion. The most important standard is to show that a clinically important assessment can be made with microarray data. The standards emerging from work on various parts of the entire process could guide the development of a workable system. However, the final standard for each component of the process depends on the accuracy required when the assay becomes part of the clinical routine: a routine that now includes the molecular evaluation of tumors.

摘要

微阵列技术在测量基因表达方面的应用,为人们提供了乐观的前景,即在癌症的临床管理中常规使用肿瘤的分子评估。基因表达阵列在标准的制定方面处于领先地位;无论是用于研究用途,还是现在用于临床应用。一些现有的标准是基于早期的认识,即微阵列技术不一致,也许不可靠。最近的实验表明,可重复性数据可以实现,临床标准也开始出现。对于肿瘤的转录评估,这意味着一个系统能够正确地对肿瘤进行采样,分离 RNA 并进行微阵列分析,评估数据,并以一致和及时的方式传达发现。最重要的标准是表明可以使用微阵列数据进行临床重要的评估。在整个过程的各个部分的工作中出现的标准,可以为开发可行的系统提供指导。然而,该过程每个组件的最终标准取决于当检测成为临床常规的一部分时所需的准确性:现在包括对肿瘤的分子评估的常规。