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。
In this study we sought to compare the utility of various databases in microarray analysis.
实验数据包含S在上述的条件下以微阵列分析测量的转录体特征。
The experimental data consists of transcriptome profiles of s under the conditions described above measured by micro array.
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。
BACKGROUND: Numerous microarray analysis programs have been created through the efforts of Open Source software development projects.
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。
The process to identify such biosignatures is called gene expression profiling, and it's done using microarray analysis.
遗传连锁作图、定位克隆、数量特性位点作图、微阵列分析及转录沉默等，是近年来常用的基因组学研究技术。
Genetic linkage mapping, positional cloning, quantitative loci traits mapping, microarrays and transcriptional silencing are genomic technologies that being widely used in recent years.
一种叫微阵列(microarray)的新仪器，即一块覆盖着DNA序列的小芯片，可以同时分析染色体上的很多点。
A new device called the microarray, a small chip coated with DNA sequences, can simultaneously analyze many more spots on the chromosomes.
微阵列数据分析的重要性在于展示功能注释和分类。
The importance of microarray data analysis lies in presenting functional annotations and classifications.
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。
These steps are described here and placed in the context of commercial and public tools available for the analysis of microarray data.
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。
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.
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。
The analysis of microarray data requires biologists to collaborate with bioinformaticians or learn the basics of statistics and programming.
这篇文章描述了分析来自微阵列实验的问题，技术和算法。
This article describes issues, techniques and algorithms for analyzing data from microarray experiments.
背景：DNA微阵列已经成为进行基因表达和表观组学的大规模分析的标准方法。
BACKGROUND: DNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics.
微阵列数据中的缺失值会对随后的数据分析造成影响。
Missing values contained in microarray data will affect subsequent analysis.
从微阵列试验中获得的大量数据不利于传统的分析方法。
The copious data arising from microarray experiments is not conducive to traditional analytical approaches.
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。
This paper presents a system of clustering analysis for DNA microarray dat…
成分和微阵列根据发明的任何分析或制备与生物分子过程的类型非常有用。
Compositions and microarrays according to the invention are useful in any type of analytical or preparative procedure relating to biomolecules.
组织微阵列是用于组织样品的高通量分析的一种研究平台。
Tissue microarrays are a platform for high-throughput analysis of tissue specimens in research.
动机：用微阵列进行基因表达分析已经成为收集全基因组功能数据的最广泛使用的高通量方法之一。
MOTIVATION: Gene expression analysis with microarrays has become one of the most widely used high-throughput methods for gathering genome-wide functional data.
分析、显示、编辑重测序微阵列数据软件。
ResqMi is a new tool for analyzing, viualizing and editing Resequencing Microarray data.
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。
The association analysis method can be applied to analyzing the microarray datasets, mining association rules of genes, and then constructing gene regulatory network.
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。
In addition, an analysis object provides methods for data processing, and an image object enables the visualisation of microarray data.
DNA微阵列技术以其高通量、自动化、平行化、快速化的优势，成为后基因组时代在转录组水平进行肿瘤基因表达谱分析的最强有力工具。
With the advantages of high throughput, automation and speed, DNA microarray is becoming the most powerful tool in cancer research in the post-genome age.
DNA微阵列技术以其高通量、自动化、平行化、快速化的优势，成为后基因组时代在转录组水平进行肿瘤基因表达谱分析的最强有力工具。
With the advantages of high throughput, automation and speed, DNA microarray is becoming the most powerful tool in cancer research in the post-genome age.
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