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。
ResqMi is a new tool for analyzing, viualizing and editing Resequencing Microarray data.
分析、显示、编辑重测序微阵列数据软件。
Objective To search an effective method on screening significant genes based on microarray data.
目的基于微阵列表达数据，探索筛选差异表达基因的有效方法。
Several classification methods based on DNA gene expression microarray data are introduced in this paper.
介绍了目前几种基于DNA微阵列基因表达数据的分类方法。
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。
The present study has focused in the identification of differentially expressed genes in microarray data.
本文着重研究差别表达基因的鉴定。
Given a microarray data set, articulate all the sources of error in measurement and find them in the particular data set.
给出一个微阵列的数据组，标示出测定中的错误的所有来源，并在特定资料组中找出它们。
The new clustering algorithm is analyzed on several aspects and tested on the published yeast cell-cycle microarray data.
从多方面分析了该算法的性能，并将该算法应用于酵母细胞周期的芯片表达谱数据聚类。
A program that compares heterologous microarray data sets, based on the number of common, differentially expressed genes.
一种程序，将外源基因芯片的数据集，基于的常见，数进行比较差异表达基因。
High throughput methodologies have increased by several orders of magnitude the amount of experimental microarray data available.
高吞吐量的方法增加了几个数量级的可用实验的微阵列数据的量。
In clinical bioinformatics, selecting appropriate software to analyze the microarray data for medical decision making is crucial.
在临床生物信息学中，选择适当的软件分析芯片数据制定医学决策是至关重要的。
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。
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。
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。
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。
BACKGROUND: When processing microarray data sets, we recently noticed that some gene names were being changed inadvertently to non-gene names.
背景：当处理微阵列数据集时，我们最近注意到一些基因名字正在被不经意地转变为非基因名字。
Recently, many algorithms have also been introduced in this field to determine gene regulatory networks based on such high-throughput microarray data.
近年来， 人们引入了多种算法来利用基因芯片数据构建基因调控网络。
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。
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。
Microarray data in different tissues showed that it has significant expression signals in head and epidermis of day-3 5th instar larvae, which was verified by RT-PCR.
基因芯片数据显示在家蚕5龄第3天的头部和体壁组织中的表达量较高，RT - PCR验证结果与此一致。
The adoption of common standards and ontologies for the management and sharing of microarray data is essential and will provide immediate benefit to the research community.
用于管理和分享微阵列数据的通用标准和本体论的采用是必要的，并将对研究社区提供直接的益处。
CONCLUSIONS: MAGE will help microarray data producers and users to exchange information by providing a common platform for data exchange, and MAGE-STK will make the adoption of MAGE easier.
结论：通过提供数据交换的一种常见平台，MAGE将帮助微阵列数据产生者和用户交换信息，并且MAGE - STK将使得MAGE的采用变得更加容易。
To search a new and effective method for feature extraction and classification based on microarray expression data.
基于微阵列表达数据，探索新的有效特征提取和分类方法。
They used microarray technology not available 15 years ago, when the data was collected.
当数据被收集时，他们应用了15年前所没有的基因芯片技术。
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。
With the extensive applications of DNA microarray technology, huge amounts of gene expression data have been generated.
随着基因芯片技术的广泛应用，产生了海量的基因表达数据。
The copious data arising from microarray experiments is not conducive to traditional analytical approaches.
从微阵列试验中获得的大量数据不利于传统的分析方法。
Microarray experiments are providing unprecedented quantities of genome-wide data on gene-expression patterns.
微阵列实验的全基因组基因表达模式的数据提供了前所未有的数量。
Although many significant results have been derived from microarray studies, one limitation has been the lack of standards for presenting and exchanging such data.
尽管很多有意义的结果已经从微阵列研究中获得，一个局限是展示和交换这些数据的标准的缺乏。
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