In this study we sought to compare the utility of various databases in microarray analysis.
在本研究中，我们力图比较微阵列分析中的多种数据库的效用。
The genes which are associated with CQ increased radiosensitivity were studied by microarray analysis.
用人类基因芯片筛选与CQ增加辐射抗拒细胞辐射敏感性相关的基因。
The results of semi-quantitative RT-PCR experiments supported the reliability of our microarray analysis.
半定量反转录-聚合酶链反应验证了芯片分析结果的可靠性。
The process to identify such biosignatures is called gene expression profiling, and it's done using microarray analysis.
确定这个生物特征的过程就是所谓的基因表达，通过微阵列分析实现。
BACKGROUND: Numerous microarray analysis programs have been created through the efforts of Open Source software development projects.
背景：很多微阵列分析程序通过开源软件开发项目的努力被创建。
In order to integrate results from individual microarray analysis to give a reliable conclusion, we propose a series of gene-set-based statistical methods.
为了整合不同基因芯片数据集的分析结果从而得出可靠的结论，本文提出了一套基于基因集的基因芯片数据分析方法。
Objective To observe the differential gene expression in human nasopharyngeal carcinoma (NPC) cell line with different radiosensitivity by cDNA microarray analysis.
目的筛选同一来源放射敏感性不同鼻咽癌细胞基因差异表达，探讨鼻咽癌放射抗拒机理。
Methods Microarray analysis on human signal transduction associated proteins was applied to profile changes in gene expression of human ECV304 endothelial cells induced by low density lipoprotein.
方法利用低密度脂蛋白作用人血管内皮细胞系ecv304，通过基因芯片分析人信号传导相关蛋白的表达谱。
There is missing value in microarray experiments and it will affect the stability and precision of the expression data analysis.
在基因芯片实验中，数据缺失客观存在，并在一定程度上影响芯片数据后续分析结果的准确性。
In microarray experiments, the missing value does exist and somewhat affect the stability and precision of the expression data analysis.
在基因芯片实验中，数据缺失客观存在，并且在一定程度上会影响芯片数据后续分析结果的准确性。
The essential and initial problem of gene expression microarray data analysis is to identify differentially expressed genes (DEGs), under certain conditions.
特定条件下的差异表达基因筛选是科学家使用芯片的初衷，也是芯片数据分析中很重要的一种应用。
Missing values contained in microarray data will affect subsequent analysis.
微阵列数据中的缺失值会对随后的数据分析造成影响。
In microarray experiments, the missing value does exist and somewhat affects the stability and precision of the expression data analysis.
在不增加实验次数的情况下，缺失值估计是降低缺失数据对后续分析影响的有效方法。
A protein microarray biosensor based on imaging ellipsometry has been developed as a high-throughput and fast technique for protein analysis.
椭偏光学生物传感器是识别和检测蛋白质的一种新型的高通量、快速生物分子分析技术。
We try to explore the method for explaining the mechanism of Cold - and Hot - ZHENG based on statistical analysis of microarray data.
本文尝试利用大规模基因芯片数据分析对中医寒热证机理进行了初步探索。
The results of RT-PCR analysis for 7 differently expressed genes were coincident with those of microarray assay.
PCR技术对其中7个基因表达差异的验证结果与基因芯片结果一致。
CD271+ MSC density was quantitated by automated image analysis of tissue microarray cores in 125 cytopenic patients: 40 lower grade MDS (<5% marrow blasts), 24 higher grade MDS, and 61 benign.
通过对125例血细胞减少症患者的组织芯片自动图像分析进行CD271+MSC密度定量检查：包括40例低级别的MDS（<5％的骨髓原始细胞），24例高级别的MDS和61例良性MDS。
In addition, an analysis object provides methods for data processing, and an image object enables the visualisation of microarray data.
此外，一个分析对象提供了用于数据处理的方法，及一个图形对象使得能够进行微阵列数据的可视化。
Gene selection is a very important problem in microarray data analysis and has critical implications for the discovery of genes related to serious diseases.
基因选择是基因芯片数据分析中的一个重要问题。
Statistical pattern recognition, with applications to face recognition, data analysis with microarray.
统计模式识别，及在面像识别，基因数据分析中的应用。
The importance of microarray data analysis lies in presenting functional annotations and classifications.
微阵列数据分析的重要性在于展示功能注释和分类。
These steps are described here and placed in the context of commercial and public tools available for the analysis of microarray data.
这里描述了这些步骤，并将它们置于商用和公共可用的微阵列数据分析环境中。
The analysis of microarray data requires biologists to collaborate with bioinformaticians or learn the basics of statistics and programming.
微阵列数据的分析需要生物学家 与生物信息学家或学习统计的基础知识和编程。
As a consequence, the scale and complexity of microarray experiments require that computer software programs do much of the data processing, storage, visualization, analysis and transfer.
因而，微阵列实验的范围和复杂性需要计算机软件程序做很多数据处理，存储，可视化，分析和转换工作。
The association analysis method can be applied to analyzing the microarray datasets, mining association rules of genes, and then constructing gene regulatory network.
关联分析方法用于分析微阵列数据集基因间相关联系，生成关联规则，进而构建基因调控网络。
This paper presents a system of clustering analysis for DNA microarray dat…
文中介绍了基因微阵列数据的聚类分析方法及其重要应用。
Secondly, using statistical significance analysis, we find out significant gene sets with respect to each microarray dataset;
然后用统计学方法找出在每组数据对应的生命过程中有显著意义的基因集；
Secondly, using statistical significance analysis, we find out significant gene sets with respect to each microarray dataset;
然后用统计学方法找出在每组数据对应的生命过程中有显著意义的基因集；
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